An Efficient Way to Recognize Faces Using Mean Embeddings

Face Recognition is one of the challenging tasks in computer vision. Recognizing various features correctly, which differs from one face to another face, is very difficult. This task becomes more difficult with the change of illumination, pose, expression, beard, age, and the quality of the picture. Before the rise of deep learning in computer vision, hand engineering feature extractor was used to recognize faces. However, they suffered mostly on a variety of illumination, pose, expression, and age. After deep learning evolution, these problems can be overcome easily by feeding a massive amount of data. Facenet is one of the deep learning architectures to recognize faces. It produces a point in a 128-dimensional vector space where the distance between two faces of the same person is small, and the distance between two faces of different persons is large. If we have a large amount of face data to compare it will be computationally expensive, and thus real-time face recognition becomes slow. Support Vector Machine or Logistic Regression can solve this problem. However, SVM or Logistic Regression cannot be able to recognize faces that not in the database as 'Unknown'. For solving these problems, we proposed a method called 'Mean Embeddings'. By using 'Mean Embeddings,' we achieved 6.5 times faster and 1.6% better performance on average with the ability to recognize unknown faces.