Celebrity recognition is one of most trending use case in computer vision space due to the growing consumption of visual content over internet. Detecting people of interest
Celebrity recognition is one of most trending use case in computer vision space due to the growing consumption of visual content over internet. Detecting people of interest (politicians, actors, sportsmen, businessmen, journalists, activists etc.) in the image or video is an interesting and challenging problem to solve. Annotated content helps businesses build better recommender systems and provide personalised user experiences.
Create named folders with multiple images per person, preferably ones with diversified facial expressions, poses, age and make-up dimensions. Enriched diversity ensures learning of disparate face encodings, which in turns boost prediction confidence in varied settings.
Encode all the detected faces in each celebrity folder. There are numerous open CV and DL based algorithms to detect facial bounding box, landmark marks and encode faces. Choose an appropriate one based on the availability of compute resources and desired performance. Save respective celebrity name, facial bounding box coordinates, encoding vector and marked image with box drawn around the face.
Every image in the dataset is passed through multitask cascaded convolutional networks to detect and align the face(s) inside. These faces are then passed through a deep CNN model to extract a 128 D feature vector.
More often than not, quite a significant amount of curated images are fallacious due to presence of multiple faces (same or different people) in a single image or misplacement of images in wrong folders.
To filter out correct face encodings per celebrity in an automated manner, graph algorithms sound promising due to their ability to discover structural relationships between components.
AI researchers at MIT and UCLA discuss the need to address the "dark matter" of computer vision, the things that aren't visible in pixels. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. What makes us humans so good at making sense of visual data? That’s a question that has preoccupied artificial intelligence and computer vision scientists for decades.
During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images…
In this article, a few image processing/computer vision problems and their solutions with python libraries (scikit-image, PIL, opencv-python) will be discussed. Some of the problems are from the exercises from this book (available on Amazon).
Image recognition and annotation technologies are evolving. New techniques that allow you to solve a wide variety of tasks quickly appear. We are happy to present five major trends in image recognition and annotation.
Nanyang Technological University researchers have developed an artificial intelligence system to recognise gestures, by using wearable strain sensors.