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.
image-processing computer-vision video-analytics artificial-intelligence celebrity-recognition artificial intelligence
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