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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.
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.
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.
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.
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 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.
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 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.
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.
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.
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 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.
[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.
Before starting the actual coding we need to import the required modules for coding. So let's import them first.
#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
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 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
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"]
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.
Labelis an integer representing the label we have assigned to that subject. So for example, folder name
s1means 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.
[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 #return only the face part of the image return gray[y:y+w, x:x+h], faces
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
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!
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.
As we know, OpenCV comes equipped with three face recognizers.
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
Still not satisfied? Want to see some action? Next step is the real action, I promise!
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)
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.
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, rect-5) return img
predict(face)method. This method will return a lable
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!
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!
Source Code: https://github.com/informramiz/opencv-face-recognition-python
License: MIT License
Real Time Object Detection in Python And OpenCV
Github Link: https://github.com/Chando0185/Object_Detection
Blog Link: https://knowledgedoctor37.blogspot.com/#
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Anomaly and fraud detection is a multi-billion-dollar industry. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry.
In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. In this article, however, I am using these terms interchangeably.
There are numerous reasons why understanding and detecting outliers are important. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance.
There is also a huge industrial application of anomaly detection. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.
A range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. While complex algorithms can be inevitable to use, sometimes simple techniques are more than enough to serve the purpose.
Below is a primer on five statistical techniques.
#anomaly-detection #machine-learning #outlier-detection #data-science #fraud-detection
Today’s article is my 5th in a series of “bite-size” article I am writing on different techniques used for anomaly detection. If you are interested, the following are the previous four articles:
Today I am going beyond statistical techniques and stepping into machine learning algorithms for anomaly detection.
#outlier-detection #fraud-detection #data-science #machine-learning #anomaly-detection
According to a recent report financial losses due to fraudulent transactions have reached about $17 billion USD, with as many as 5% of consumers experiencing fraud incidents of some kind.
In light of such a big volume of financial losses, every industry is taking fraud detection seriously. It’s not just the financial industries that are susceptible, anomalies are prevalent in every single industry and can take many different forms — such as network intrusion, disturbances in business performances and abrupt changes in KPIs etc.
Fraud/anomaly/outlier detection has long been the subject of intense research in data science. In the ever-changing landscape of fraud detection, new tools and techniques are being tested and employed every day to screen out abnormalities. In this series of articles, so far I’ve discussed six different techniques for fraud detection:
Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise.
As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm. It takes multi-dimensional data as inputs and clusters them according to the model parameters — e.g. epsilon and minimum samples. Based on these parameters, the algorithm determines whether certain values in the dataset are outliers or not.
Below is a simple demonstration in Python programming language.
#fraud-detection #machine-learning #anomaly-detection #outlier-detection #data-science