Jerad  Bailey

Jerad Bailey

1596478020

Deepfake detection is super hard!

Recent advancements in artificial intelligence (AI) and cloud computing technologies have led to rapid development in the sophistication of audio, video, and image manipulation techniques. This synthetic media content is commonly referred to as “deepfakes[1].” AI based tools can manipulate media in increasingly believable ways, for example by creating a copy of a public person’s voice or superimposing one person’s face on another person’s body.

Legislation, policy, media literacy, and technology must work in tandem for an effective remedy for malicious use of deepfakes.

Technical countermeasures used to mitigate the impact of deepfakes fall into three categories: media authentication, media provenance, and deepfake detection.

Media Authentication includes solutions that help prove integrity across the media lifecycle by using watermarking, media verification markers, signatures, and chain-of-custody logging. Authentication is the most effective way to prevent the deceptive manipulation of trusted media because it verifies and tracks integrity throughout the content lifecycle or verify it at the distribution endpoint.

#ai #fb #deepfake-technology #dfdc #deepfakes

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Deepfake detection is super hard!

Super Affiliate System Review - Recommended or Not?

Is it worth your money?

John Crestani created the Super Affiliate System, an ideal program to equip people with information and skills to achieve affiliate marketing success. In this system, learners need to participate in a module-based learning setting that will help them get started with affiliate marketing by using a simplified system that consists of a single website, buyers, and regular quality traffic. Go through the super affiliate system review to find out more!

John Crestanis’s extensive knowledge and skills in this industry set the Super Affiliate System far apart from competitor affiliate marketing systems. But is the Super Affiliate Commission System a genuine deal? Is it worth investing in? Today, in this Super Affiliate System review, we will take a look at what the system requires and decide whether it’s a real deal affiliate marketing enthusiasts should invest in.

What is the Super Affiliate System?

This is a complete training course that assists people in becoming successful affiliate marketers. The guide uses videos to lead you through the tools and processes you need to become a super affiliate marketer. The program creator has shared thriving, in-depth strategies to give you a life of freedom if you pay heed to them.

The Super Affiliate System is a training guide to equip you with knowledge and skills in the industry. The system will also allow a list of tools needed for affiliate marketers to fast-track their potential.

Super Affiliate System Review: Pros and Cons

There are a few pros and cons that will enlighten beginner affiliates on whether to consider this system or not. Let’s have a look at them one by one:
Pros:-

The system has extensive and informative, easy to follow modules.

The system is designed in a user-friendly manner, especially for beginners.

Equipped with video tutorials to quickly guide you through the process.

The system gives affiliates niche information to provide them with a competitive advantage.

Equipped with revision sections, weekly questions, and daily assignments to help you grasp all the course ideas.

The system extends clients to a 24/7 support system.

It allows clients to have monthly payment plans that can be suitable for those who can’t bear the price of a single down payment. It offers

clients a lot of bonuses.

Clients are allowed a 60-day Super Affiliate System refund guarantee.

Cons:-

It’s very expensive.

Limited coverage of affiliate networks and niches.

Who created the Super Affiliate System?

John Crestani, a 29-year-old expert in affiliate marketing from Santa Monica, California, is the program’s creator. The veteran left out of college and chose to earn money online since there are low job prospects. He failed several times, striving to make ends meet for quite some time until he successfully built a successful affiliate site dealing with health-related products.
He is currently a seven-figure person making more than $500 per month. His remarkable success in affiliate marketing has made him a featured in Yahoo Finance, Inc., Forbes, Business Insider, and Home Business magazine.

With the enormous success he has seen in affiliate marketing, John has designed an easy-to-follow guide to provide people with the skills to make money as an affiliate marketer. He has described all the strategies and tools he used to lead him to success.

Super Affiliate System Review: Does it Work?

The system accommodates affiliate marketers with in-depth details on how to develop successful affiliate networks. The Super Affiliate System review has a positive impact on different affiliate marketers who have tried it and noticed impressive results. But then, does it work?

The program doesn’t promise you overnight riches; it demands work and application to perform it. After finishing the Super Affiliate System online video training course, attaining success requires you to put John’s strategies into practice. A lot of commitment, hard work, and time are required in order to become a successful affiliate marketer.

How Does It Work?

As its name suggests, the Super Affiliate System is there to make you a super affiliate. John himself is an experienced affiliate, and he has accumulated all the necessary tools to achieve success in training others to become super affiliates. The Super Affiliate Network System members’ area has outlined everything that the veteran affiliate used to make millions as an affiliate.
The guide will help you set up campaigns, traffic resources, essential tools you need as an affiliate, and the veteran affiliate networks to achieve success.

Most amateur affiliates usually get frustrated as they might demand time to start making money. Those who succeed in getting little coins mainly do the following to earn;

They first become Super Affiliate System affiliates.

They promote the Super Affiliate System in multiple ways.

They convert the marketing leads they get into sales.

They receive a commission on every sale they make.

Affiliate marketing involves trading other people’s products and earning commissions from the sales you make. It’s an online business that can be done either with free or paid traffic. With the Super Affiliate System, one of the basic teachings you’ll get in the guide is how to make money by promoting the course itself using paid traffic Facebook ads.

What’s in the Super Affiliate System?

The system is amongst the most comprehensive affiliate marketing courses on the market. The Super Affiliate System comprises more than 50 hours of content that takes about six weeks to complete. The Super Affiliate System also includes several video lectures and tutorials alongside several questions and homework assignments to test its retention.

What Does the Super Affiliate Program Cover?

This program aims to provide affiliates with comprehensive ideas and tactics to become successful affiliate marketers. Therefore, their online video training course is comprehensive. Below are areas of information included within the modules;

Facebook ads

Native ads

Website creation

Google ads

Social ads

Niche selection

YouTube ads

Content creation

Scaling

Tracking and testing

Affiliate networks

Click funnels

Advanced strategies

Besides the extensive information the creator has presented on these topics, he also went an extra mile to review the complete material and also guide marketers through the course.

Who is the Super Affiliate System for?

There are a number of digital products out there that provide solutions to techniques to earn money online. But not all options offer real value for what you want. John gives people a Super Affiliate System free webinar to allow them to learn what the system entails. It will help if you spare time to watch it, as it takes 90 minutes to get through. 
Below is a brief guide to who this system is for:

  1. It is for beginners who can equip themselves with appropriate affiliate marketing skills. People who are still employed and want to have an alternative earning scheme fit here.

  2. The system is also suitable for entrepreneurs who need to learn to earn money online, mainly using paid ads.

  3. The Super Affiliate System also suits anyone who is looking for another alternative stream of income.

Making money online has many advantages at large. You have the flexibility to work from any place, in the comfort of your home, with just an internet connection. Even though John has stated that there are no special skills needed to achieve success in affiliate marketing, there are little basics necessary to keep you on track. 
Having a proper mindset is also vital to attaining success in affiliate marketing. So, affiliates who believe in the system working for them need to be dedicated, focused, and committed. 
They incorporate;

Keep in mind that you have more than $895 in advertisements to get started. Furthermore, set aside a couple of dollars so that you keep on the right track.
There is also additional software you require to get started. It needs an extra of between $80 and $100 a month to get it.

Where to Buy a Super Affiliate System?

If you are interested in joining this big team, you have to get into the Super Affiliate System on the official website, superaffiliatesystem.org, and get it from there. You have to pay their set fees to get their courses and other new materials within their learning scope.

Super Affiliate System Review: Is it Worth the Money?

It depends on an individual whether the system is worth it or not. The system is worth the money for serious people who want to go deep into an affiliate marketing career and have the time to put the Super Affiliate System strategies into practice. Super Affiliate System Review, Is it worth your money?
But people who also look forward to becoming rich overnight need to get off as this is not your way. Hard work and commitment are paramount to getting everything that works best for you.

**Visit The Officail Website

#super affiliate system review #super affiliate system #super affiliate system 3 #super affiliate system 3.0 review #super affiliate system pro #super affiliate system john crestani

Jerad  Bailey

Jerad Bailey

1596478020

Deepfake detection is super hard!

Recent advancements in artificial intelligence (AI) and cloud computing technologies have led to rapid development in the sophistication of audio, video, and image manipulation techniques. This synthetic media content is commonly referred to as “deepfakes[1].” AI based tools can manipulate media in increasingly believable ways, for example by creating a copy of a public person’s voice or superimposing one person’s face on another person’s body.

Legislation, policy, media literacy, and technology must work in tandem for an effective remedy for malicious use of deepfakes.

Technical countermeasures used to mitigate the impact of deepfakes fall into three categories: media authentication, media provenance, and deepfake detection.

Media Authentication includes solutions that help prove integrity across the media lifecycle by using watermarking, media verification markers, signatures, and chain-of-custody logging. Authentication is the most effective way to prevent the deceptive manipulation of trusted media because it verifies and tracks integrity throughout the content lifecycle or verify it at the distribution endpoint.

#ai #fb #deepfake-technology #dfdc #deepfakes

Face Recognition with OpenCV and Python

Introduction

What is face recognition? Or what is recognition? When you look at an apple fruit, your mind immediately tells you that this is an apple fruit. This process, your mind telling you that this is an apple fruit is recognition in simple words. So what is face recognition then? I am sure you have guessed it right. When you look at your friend walking down the street or a picture of him, you recognize that he is your friend Paulo. Interestingly when you look at your friend or a picture of him you look at his face first before looking at anything else. Ever wondered why you do that? This is so that you can recognize him by looking at his face. Well, this is you doing face recognition.

But the real question is how does face recognition works? It is quite simple and intuitive. Take a real life example, when you meet someone first time in your life you don't recognize him, right? While he talks or shakes hands with you, you look at his face, eyes, nose, mouth, color and overall look. This is your mind learning or training for the face recognition of that person by gathering face data. Then he tells you that his name is Paulo. At this point your mind knows that the face data it just learned belongs to Paulo. Now your mind is trained and ready to do face recognition on Paulo's face. Next time when you will see Paulo or his face in a picture you will immediately recognize him. This is how face recognition work. The more you will meet Paulo, the more data your mind will collect about Paulo and especially his face and the better you will become at recognizing him.

Now the next question is how to code face recognition with OpenCV, after all this is the only reason why you are reading this article, right? OK then. You might say that our mind can do these things easily but to actually code them into a computer is difficult? Don't worry, it is not. Thanks to OpenCV, coding face recognition is as easier as it feels. The coding steps for face recognition are same as we discussed it in real life example above.

  • Training Data Gathering: Gather face data (face images in this case) of the persons you want to recognize
  • Training of Recognizer: Feed that face data (and respective names of each face) to the face recognizer so that it can learn.
  • Recognition: Feed new faces of the persons and see if the face recognizer you just trained recognizes them.

OpenCV comes equipped with built in face recognizer, all you have to do is feed it the face data. It's that simple and this how it will look once we are done coding it.

visualization

OpenCV Face Recognizers

OpenCV has three built in face recognizers and thanks to OpenCV's clean coding, you can use any of them by just changing a single line of code. Below are the names of those face recognizers and their OpenCV calls.

  1. EigenFaces Face Recognizer Recognizer - cv2.face.createEigenFaceRecognizer()
  2. FisherFaces Face Recognizer Recognizer - cv2.face.createFisherFaceRecognizer()
  3. Local Binary Patterns Histograms (LBPH) Face Recognizer - cv2.face.createLBPHFaceRecognizer()

We have got three face recognizers but do you know which one to use and when? Or which one is better? I guess not. So why not go through a brief summary of each, what you say? I am assuming you said yes :) So let's dive into the theory of each.

EigenFaces Face Recognizer

This algorithm considers the fact that not all parts of a face are equally important and equally useful. When you look at some one you recognize him/her by his distinct features like eyes, nose, cheeks, forehead and how they vary with respect to each other. So you are actually focusing on the areas of maximum change (mathematically speaking, this change is variance) of the face. For example, from eyes to nose there is a significant change and same is the case from nose to mouth. When you look at multiple faces you compare them by looking at these parts of the faces because these parts are the most useful and important components of a face. Important because they catch the maximum change among faces, change the helps you differentiate one face from the other. This is exactly how EigenFaces face recognizer works.

EigenFaces face recognizer looks at all the training images of all the persons as a whole and try to extract the components which are important and useful (the components that catch the maximum variance/change) and discards the rest of the components. This way it not only extracts the important components from the training data but also saves memory by discarding the less important components. These important components it extracts are called principal components. Below is an image showing the principal components extracted from a list of faces.

Principal Components eigenfaces_opencv source

You can see that principal components actually represent faces and these faces are called eigen faces and hence the name of the algorithm.

So this is how EigenFaces face recognizer trains itself (by extracting principal components). Remember, it also keeps a record of which principal component belongs to which person. One thing to note in above image is that Eigenfaces algorithm also considers illumination as an important component.

Later during recognition, when you feed a new image to the algorithm, it repeats the same process on that image as well. It extracts the principal component from that new image and compares that component with the list of components it stored during training and finds the component with the best match and returns the person label associated with that best match component.

Easy peasy, right? Next one is easier than this one.

FisherFaces Face Recognizer

This algorithm is an improved version of EigenFaces face recognizer. Eigenfaces face recognizer looks at all the training faces of all the persons at once and finds principal components from all of them combined. By capturing principal components from all the of them combined you are not focusing on the features that discriminate one person from the other but the features that represent all the persons in the training data as a whole.

This approach has drawbacks, for example, images with sharp changes (like light changes which is not a useful feature at all) may dominate the rest of the images and you may end up with features that are from external source like light and are not useful for discrimination at all. In the end, your principal components will represent light changes and not the actual face features.

Fisherfaces algorithm, instead of extracting useful features that represent all the faces of all the persons, it extracts useful features that discriminate one person from the others. This way features of one person do not dominate over the others and you have the features that discriminate one person from the others.

Below is an image of features extracted using Fisherfaces algorithm.

Fisher Faces eigenfaces_opencv source

You can see that features extracted actually represent faces and these faces are called fisher faces and hence the name of the algorithm.

One thing to note here is that even in Fisherfaces algorithm if multiple persons have images with sharp changes due to external sources like light they will dominate over other features and affect recognition accuracy.

Getting bored with this theory? Don't worry, only one face recognizer is left and then we will dive deep into the coding part.

Local Binary Patterns Histograms (LBPH) Face Recognizer

I wrote a detailed explaination on Local Binary Patterns Histograms in my previous article on face detection using local binary patterns histograms. So here I will just give a brief overview of how it works.

We know that Eigenfaces and Fisherfaces are both affected by light and in real life we can't guarantee perfect light conditions. LBPH face recognizer is an improvement to overcome this drawback.

Idea is to not look at the image as a whole instead find the local features of an image. LBPH alogrithm try to find the local structure of an image and it does that by comparing each pixel with its neighboring pixels.

Take a 3x3 window and move it one image, at each move (each local part of an image), compare the pixel at the center with its neighbor pixels. The neighbors with intensity value less than or equal to center pixel are denoted by 1 and others by 0. Then you read these 0/1 values under 3x3 window in a clockwise order and you will have a binary pattern like 11100011 and this pattern is local to some area of the image. You do this on whole image and you will have a list of local binary patterns.

LBP Labeling LBP labeling

Now you get why this algorithm has Local Binary Patterns in its name? Because you get a list of local binary patterns. Now you may be wondering, what about the histogram part of the LBPH? Well after you get a list of local binary patterns, you convert each binary pattern into a decimal number (as shown in above image) and then you make a histogram of all of those values. A sample histogram looks like this.

Sample Histogram LBP labeling

I guess this answers the question about histogram part. So in the end you will have one histogram for each face image in the training data set. That means if there were 100 images in training data set then LBPH will extract 100 histograms after training and store them for later recognition. Remember, algorithm also keeps track of which histogram belongs to which person.

Later during recognition, when you will feed a new image to the recognizer for recognition it will generate a histogram for that new image, compare that histogram with the histograms it already has, find the best match histogram and return the person label associated with that best match histogram. 

Below is a list of faces and their respective local binary patterns images. You can see that the LBP images are not affected by changes in light conditions.

LBP Faces LBP faces source

The theory part is over and now comes the coding part! Ready to dive into coding? Let's get into it then.

Coding Face Recognition with OpenCV

The Face Recognition process in this tutorial is divided into three steps.

  1. Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
  2. Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
  3. Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.

[There should be a visualization diagram for above steps here]

To detect faces, I will use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its Python coding.

Import Required Modules

Before starting the actual coding we need to import the required modules for coding. So let's import them first.

  • cv2: is OpenCV module for Python which we will use for face detection and face recognition.
  • os: We will use this Python module to read our training directories and file names.
  • numpy: We will use this module to convert Python lists to numpy arrays as OpenCV face recognizers accept numpy arrays.
#import OpenCV module
import cv2
#import os module for reading training data directories and paths
import os
#import numpy to convert python lists to numpy arrays as 
#it is needed by OpenCV face recognizers
import numpy as np

#matplotlib for display our images
import matplotlib.pyplot as plt
%matplotlib inline 

Training Data

The more images used in training the better. Normally a lot of images are used for training a face recognizer so that it can learn different looks of the same person, for example with glasses, without glasses, laughing, sad, happy, crying, with beard, without beard etc. To keep our tutorial simple we are going to use only 12 images for each person.

So our training data consists of total 2 persons with 12 images of each person. All training data is inside training-data folder. training-data folder contains one folder for each person and each folder is named with format sLabel (e.g. s1, s2) where label is actually the integer label assigned to that person. For example folder named s1 means that this folder contains images for person 1. The directory structure tree for training data is as follows:

training-data
|-------------- s1
|               |-- 1.jpg
|               |-- ...
|               |-- 12.jpg
|-------------- s2
|               |-- 1.jpg
|               |-- ...
|               |-- 12.jpg

The test-data folder contains images that we will use to test our face recognizer after it has been successfully trained.

As OpenCV face recognizer accepts labels as integers so we need to define a mapping between integer labels and persons actual names so below I am defining a mapping of persons integer labels and their respective names.

Note: As we have not assigned label 0 to any person so the mapping for label 0 is empty.

#there is no label 0 in our training data so subject name for index/label 0 is empty
subjects = ["", "Tom Cruise", "Shahrukh Khan"]

Prepare training data

You may be wondering why data preparation, right? Well, OpenCV face recognizer accepts data in a specific format. It accepts two vectors, one vector is of faces of all the persons and the second vector is of integer labels for each face so that when processing a face the face recognizer knows which person that particular face belongs too.

For example, if we had 2 persons and 2 images for each person.

PERSON-1    PERSON-2   

img1        img1         
img2        img2

Then the prepare data step will produce following face and label vectors.

FACES                        LABELS

person1_img1_face              1
person1_img2_face              1
person2_img1_face              2
person2_img2_face              2

Preparing data step can be further divided into following sub-steps.

  1. Read all the folder names of subjects/persons provided in training data folder. So for example, in this tutorial we have folder names: s1, s2.
  2. For each subject, extract label number. Do you remember that our folders have a special naming convention? Folder names follow the format sLabel where Label is an integer representing the label we have assigned to that subject. So for example, folder name s1 means that the subject has label 1, s2 means subject label is 2 and so on. The label extracted in this step is assigned to each face detected in the next step.
  3. Read all the images of the subject, detect face from each image.
  4. Add each face to faces vector with corresponding subject label (extracted in above step) added to labels vector.

[There should be a visualization for above steps here]

Did you read my last article on face detection? No? Then you better do so right now because to detect faces, I am going to use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its coding. Below is the same code.

#function to detect face using OpenCV
def detect_face(img):
    #convert the test image to gray image as opencv face detector expects gray images
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    #load OpenCV face detector, I am using LBP which is fast
    #there is also a more accurate but slow Haar classifier
    face_cascade = cv2.CascadeClassifier('opencv-files/lbpcascade_frontalface.xml')

    #let's detect multiscale (some images may be closer to camera than others) images
    #result is a list of faces
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
    
    #if no faces are detected then return original img
    if (len(faces) == 0):
        return None, None
    
    #under the assumption that there will be only one face,
    #extract the face area
    (x, y, w, h) = faces[0]
    
    #return only the face part of the image
    return gray[y:y+w, x:x+h], faces[0]

I am using OpenCV's LBP face detector. On line 4, I convert the image to grayscale because most operations in OpenCV are performed in gray scale, then on line 8 I load LBP face detector using cv2.CascadeClassifier class. After that on line 12 I use cv2.CascadeClassifier class' detectMultiScale method to detect all the faces in the image. on line 20, from detected faces I only pick the first face because in one image there will be only one face (under the assumption that there will be only one prominent face). As faces returned by detectMultiScale method are actually rectangles (x, y, width, height) and not actual faces images so we have to extract face image area from the main image. So on line 23 I extract face area from gray image and return both the face image area and face rectangle.

Now you have got a face detector and you know the 4 steps to prepare the data, so are you ready to code the prepare data step? Yes? So let's do it.

#this function will read all persons' training images, detect face from each image
#and will return two lists of exactly same size, one list 
# of faces and another list of labels for each face
def prepare_training_data(data_folder_path):
    
    #------STEP-1--------
    #get the directories (one directory for each subject) in data folder
    dirs = os.listdir(data_folder_path)
    
    #list to hold all subject faces
    faces = []
    #list to hold labels for all subjects
    labels = []
    
    #let's go through each directory and read images within it
    for dir_name in dirs:
        
        #our subject directories start with letter 's' so
        #ignore any non-relevant directories if any
        if not dir_name.startswith("s"):
            continue;
            
        #------STEP-2--------
        #extract label number of subject from dir_name
        #format of dir name = slabel
        #, so removing letter 's' from dir_name will give us label
        label = int(dir_name.replace("s", ""))
        
        #build path of directory containin images for current subject subject
        #sample subject_dir_path = "training-data/s1"
        subject_dir_path = data_folder_path + "/" + dir_name
        
        #get the images names that are inside the given subject directory
        subject_images_names = os.listdir(subject_dir_path)
        
        #------STEP-3--------
        #go through each image name, read image, 
        #detect face and add face to list of faces
        for image_name in subject_images_names:
            
            #ignore system files like .DS_Store
            if image_name.startswith("."):
                continue;
            
            #build image path
            #sample image path = training-data/s1/1.pgm
            image_path = subject_dir_path + "/" + image_name

            #read image
            image = cv2.imread(image_path)
            
            #display an image window to show the image 
            cv2.imshow("Training on image...", image)
            cv2.waitKey(100)
            
            #detect face
            face, rect = detect_face(image)
            
            #------STEP-4--------
            #for the purpose of this tutorial
            #we will ignore faces that are not detected
            if face is not None:
                #add face to list of faces
                faces.append(face)
                #add label for this face
                labels.append(label)
            
    cv2.destroyAllWindows()
    cv2.waitKey(1)
    cv2.destroyAllWindows()
    
    return faces, labels

I have defined a function that takes the path, where training subjects' folders are stored, as parameter. This function follows the same 4 prepare data substeps mentioned above.

(step-1) On line 8 I am using os.listdir method to read names of all folders stored on path passed to function as parameter. On line 10-13 I am defining labels and faces vectors.

(step-2) After that I traverse through all subjects' folder names and from each subject's folder name on line 27 I am extracting the label information. As folder names follow the sLabel naming convention so removing the letter s from folder name will give us the label assigned to that subject.

(step-3) On line 34, I read all the images names of of the current subject being traversed and on line 39-66 I traverse those images one by one. On line 53-54 I am using OpenCV's imshow(window_title, image) along with OpenCV's waitKey(interval) method to display the current image being traveresed. The waitKey(interval) method pauses the code flow for the given interval (milliseconds), I am using it with 100ms interval so that we can view the image window for 100ms. On line 57, I detect face from the current image being traversed.

(step-4) On line 62-66, I add the detected face and label to their respective vectors.

But a function can't do anything unless we call it on some data that it has to prepare, right? Don't worry, I have got data of two beautiful and famous celebrities. I am sure you will recognize them!

training-data

Let's call this function on images of these beautiful celebrities to prepare data for training of our Face Recognizer. Below is a simple code to do that.

#let's first prepare our training data
#data will be in two lists of same size
#one list will contain all the faces
#and other list will contain respective labels for each face
print("Preparing data...")
faces, labels = prepare_training_data("training-data")
print("Data prepared")

#print total faces and labels
print("Total faces: ", len(faces))
print("Total labels: ", len(labels))
Preparing data...
Data prepared
Total faces:  23
Total labels:  23

This was probably the boring part, right? Don't worry, the fun stuff is coming up next. It's time to train our own face recognizer so that once trained it can recognize new faces of the persons it was trained on. Read? Ok then let's train our face recognizer.

Train Face Recognizer

As we know, OpenCV comes equipped with three face recognizers.

  1. EigenFace Recognizer: This can be created with cv2.face.createEigenFaceRecognizer()
  2. FisherFace Recognizer: This can be created with cv2.face.createFisherFaceRecognizer()
  3. Local Binary Patterns Histogram (LBPH): This can be created with cv2.face.LBPHFisherFaceRecognizer()

I am going to use LBPH face recognizer but you can use any face recognizer of your choice. No matter which of the OpenCV's face recognizer you use the code will remain the same. You just have to change one line, the face recognizer initialization line given below.

#create our LBPH face recognizer 
face_recognizer = cv2.face.createLBPHFaceRecognizer()

#or use EigenFaceRecognizer by replacing above line with 
#face_recognizer = cv2.face.createEigenFaceRecognizer()

#or use FisherFaceRecognizer by replacing above line with 
#face_recognizer = cv2.face.createFisherFaceRecognizer()

Now that we have initialized our face recognizer and we also have prepared our training data, it's time to train the face recognizer. We will do that by calling the train(faces-vector, labels-vector) method of face recognizer.

#train our face recognizer of our training faces
face_recognizer.train(faces, np.array(labels))

Did you notice that instead of passing labels vector directly to face recognizer I am first converting it to numpy array? This is because OpenCV expects labels vector to be a numpy array.

Still not satisfied? Want to see some action? Next step is the real action, I promise!

Prediction

Now comes my favorite part, the prediction part. This is where we actually get to see if our algorithm is actually recognizing our trained subjects's faces or not. We will take two test images of our celeberities, detect faces from each of them and then pass those faces to our trained face recognizer to see if it recognizes them.

Below are some utility functions that we will use for drawing bounding box (rectangle) around face and putting celeberity name near the face bounding box.

#function to draw rectangle on image 
#according to given (x, y) coordinates and 
#given width and heigh
def draw_rectangle(img, rect):
    (x, y, w, h) = rect
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
#function to draw text on give image starting from
#passed (x, y) coordinates. 
def draw_text(img, text, x, y):
    cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)

First function draw_rectangle draws a rectangle on image based on passed rectangle coordinates. It uses OpenCV's built in function cv2.rectangle(img, topLeftPoint, bottomRightPoint, rgbColor, lineWidth) to draw rectangle. We will use it to draw a rectangle around the face detected in test image.

Second function draw_text uses OpenCV's built in function cv2.putText(img, text, startPoint, font, fontSize, rgbColor, lineWidth) to draw text on image.

Now that we have the drawing functions, we just need to call the face recognizer's predict(face) method to test our face recognizer on test images. Following function does the prediction for us.

#this function recognizes the person in image passed
#and draws a rectangle around detected face with name of the 
#subject
def predict(test_img):
    #make a copy of the image as we don't want to chang original image
    img = test_img.copy()
    #detect face from the image
    face, rect = detect_face(img)

    #predict the image using our face recognizer 
    label= face_recognizer.predict(face)
    #get name of respective label returned by face recognizer
    label_text = subjects[label]
    
    #draw a rectangle around face detected
    draw_rectangle(img, rect)
    #draw name of predicted person
    draw_text(img, label_text, rect[0], rect[1]-5)
    
    return img
  • line-6 read the test image
  • line-7 detect face from test image
  • line-11 recognize the face by calling face recognizer's predict(face) method. This method will return a lable
  • line-12 get the name associated with the label
  • line-16 draw rectangle around the detected face
  • line-18 draw name of predicted subject above face rectangle

Now that we have the prediction function well defined, next step is to actually call this function on our test images and display those test images to see if our face recognizer correctly recognized them. So let's do it. This is what we have been waiting for.

print("Predicting images...")

#load test images
test_img1 = cv2.imread("test-data/test1.jpg")
test_img2 = cv2.imread("test-data/test2.jpg")

#perform a prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print("Prediction complete")

#create a figure of 2 plots (one for each test image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

#display test image1 result
ax1.imshow(cv2.cvtColor(predicted_img1, cv2.COLOR_BGR2RGB))

#display test image2 result
ax2.imshow(cv2.cvtColor(predicted_img2, cv2.COLOR_BGR2RGB))

#display both images
cv2.imshow("Tom cruise test", predicted_img1)
cv2.imshow("Shahrukh Khan test", predicted_img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
Predicting images...
Prediction complete

wohooo! Is'nt it beautiful? Indeed, it is!

End Notes

Face Recognition is a fascinating idea to work on and OpenCV has made it extremely simple and easy for us to code it. It just takes a few lines of code to have a fully working face recognition application and we can switch between all three face recognizers with a single line of code change. It's that simple.

Although EigenFaces, FisherFaces and LBPH face recognizers are good but there are even better ways to perform face recognition like using Histogram of Oriented Gradients (HOGs) and Neural Networks. So the more advanced face recognition algorithms are now a days implemented using a combination of OpenCV and Machine learning. I have plans to write some articles on those more advanced methods as well, so stay tuned!

Download Details:
Author: informramiz
Source Code: https://github.com/informramiz/opencv-face-recognition-python
License: MIT License

#opencv  #python #facerecognition 

Chando Dhar

Chando Dhar

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Deep Learning Project : Real Time Object Detection in Python & Opencv

Real Time Object Detection in Python And OpenCV

Github Link: https://github.com/Chando0185/Object_Detection

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What is HARD Protocol (HARD) | What is HARD Protocol token | What is HARD token

What is the HARD Protocol?

source : TokenClan

HARD is the world’s first cross-chain money market that enables you to earn more with your digital assets. With HARD you will now be able to lend, borrow, and earn with assets like BTC, XRP, BNB, BUSD, KAVA, and USDX.

It is the first of what will be many applications that utilize the Kava blockchain’s security, price feed module, and cross-chain functionality to provide open and decentralized financial services to the world.

How does the HARD Protocol Work?

There are three major activities:

  1. Supply — You can safely supply your digital assets on HARD and earn interest.
  2. Borrow — You can use your digital assets as collateral to borrow others.
  3. Earn — Suppliers and borrowers earn HARD, the governance token of the application.

How the HARD Protocol is Built

HARD is an application built on Kava, as such, it leverages Kava’s existing validators for security, bridges for cross-chain asset transfer, and partners services such as Chainlink oracles for price-reference data.

Version 1 of the HARD Protocol ships with support for supply-side deposits and HARD incentives for BTC, XRP, BNB, BUSD, USDX. Version 2 ships with borrow functionality and borrow-side incentives for those assets plus expanded functionality of HARD governance on-chain.

Initial Assets — BTC, BNB, BUSD, USDX, XRP, and HARD.

As an application built on Kava, it will have access to any asset on the Kava blockchain. In the Kava-4 “Gateway” mainnet upgrade, the BEP3 Bridge will be expanded to support BTC, XRP, BUSD and others making these assets available for use in HARD money markets along with native Kava assets like KAVA, HARD and USDX.

Open Integrations

Built as an open and permissionless application, HARD is accessible by anyone, anytime, anywhere in the world. Exchanges, Fintech apps, and financial institutions can integrate HARD’s money market products and provide earning and borrowing opportunities directly to their users.

HARD Governance

As seen in all decentralized money market applications today, a governance token is necessary for proper decentralization and to ensure the ongoing evolution of the application. To compete in the current environment it’s also critical to have incentives to bootstrap liquidity and incentivize user participation.

The HARD token — decentralized governance and incentives.

The HARD token primary role is to give holders a voice in the platform. Collectively, HARD holders are responsible for managing key parameters of the protocol such as what assets are to be offered, how rewards are distributed amongst assets, as well as set any platform fees, etc.

HARD tokens will also be used to incentivize early participants giving them a voice in the ongoing evolution and management of the application.

KAVA Tokens and Compensation

In designing HARD, it was carefully evaluated if the KAVA token could be used to also govern the application. Three major items prevented this:

  1. HARD’s evolution needs to be driven by the participants that use it. We believe that a fair distribution to users is necessary for long term success. The users of HARD money markets may or may not be the same as those that hold KAVA today giving reason to not conflate the governance of the Kava blockchain with that of the application.
  2. Having supplier and borrower incentives is a must in today’s yield oriented DeFi market. If we used the KAVA token for incentives on HARD, we would need to inflate KAVA upwards of 50% supply. Given that not all KAVA holders would be participants on HARD, inflating KAVA would meaningfully dilute existing KAVA holders to a degree that is not acceptable.
  3. Lastly, the KAVA token needs to be preserved as a reserve asset responsible for recapitalizing the lending platform. Conflating its value in multiple use cases creates a cascade of problems and can potentially undermine its value as a reserve asset.

HARD Compensation for KAVA Stakers

HARD and any other applications that utilizes the Kava blockchain’s security should in theory compensate KAVA stakers directly for that security and cross-chain infrastructure. As such, we felt it was appropriate that HARD tokens should be distributed continuously, pro-rata, amongst KAVA stakers.

HARD Distribution

Image for post

A detailed release schedule can be found on this spreadsheet

There will be a max supply of 200M HARD tokens. The distribution of HARD tokens will be as follows:

40% — Incentives for Suppliers & Borrowers

25% — Treasury

20% — Kava Stakers

10% — Team

5% — IEO

Note: To achieve a fair distribution there will not be any seed or private sale of the HARD tokens.

Development Roadmap

  1. September 21st, 2020 — Testnet of HARD v1
  • Internal testing.
  • External audit.

2. October 15th, 2020 — HARD v1 ships along with Kava-4 “Gateway” upgrade

  • HARD distribution begins.
  • Supply-side deposits and HARD incentives for BTC, BNB, HARD, & USDX begin.

3. December 30th, 2020 — HARD v2

  • Expanded HARD governance.
  • Supply & borrow — BTC, XRP, BNB, BUSD, USDX and LINK.
  • Borrow-side incentives begin — BTC, BNB, BUSD, LINK, USDX & XRP.

Learn about Cryptocurrency in this article ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner

Would you like to earn HARD right now! ☞ CLICK HERE

How and Where to Buy HARD?

HARD has been listed on a number of crypto exchanges, unlike other main cryptocurrencies, it cannot be directly purchased with fiats money. However, You can still easily buy this coin by first buying Bitcoin, ETH, USDT from any large exchanges and then transfer to the exchange that offers to trade this coin, in this guide article we will walk you through in detail the steps to buy HARD

You will have to first buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT)…

We will use Binance Exchange here as it is one of the largest crypto exchanges that accept fiat deposits.

Binance is a popular cryptocurrency exchange which was started in China but then moved their headquarters to the crypto-friendly Island of Malta in the EU. Binance is popular for its crypto to crypto exchange services. Binance exploded onto the scene in the mania of 2017 and has since gone on to become the top crypto exchange in the world.

Once you finished the KYC process. You will be asked to add a payment method. Here you can either choose to provide a credit/debit card or use a bank transfer, and buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT)

SIGN UP ON BINANCE

Step by Step Guide : What is Binance | How to Create an account on Binance (Updated 2021)

After the deposit is confirmed you may then purchase HARD from the exchange.

Exchange: Binance, WBF Exchange, Gate.io, MXC.COM, and Coinone

Apart from the exchange(s) above, there are a few popular crypto exchanges where they have decent daily trading volumes and a huge user base. This will ensure you will be able to sell your coins at any time and the fees will usually be lower. It is suggested that you also register on these exchanges since once HARD gets listed there it will attract a large amount of trading volumes from the users there, that means you will be having some great trading opportunities!

Top exchanges for token-coin trading. Follow instructions and make unlimited money

https://www.binance.com
https://www.bittrex.com
https://www.poloniex.com
https://www.bitfinex.com
https://www.huobi.com
https://www.mxc.ai
https://www.probit.com
https://www.gate.io
https://www.coinbase.com

Find more information HARD

☞ Website
☞ Explorer
☞ Social Channel
☞ Coinmarketcap

Thank for visiting and reading this article! I’m highly appreciate your actions! Please share if you liked it!

#blockchain #bitcoin #cryptocurrency #hard protocol #hard