Efficient video face recognition by using Fisher Vector encoding of binary features

One of the main problems of recognizing faces in videos is to achieve accurate algorithms which can be used in real-time applications. Recently, Fisher Vector representation of local descriptors (e.g., SIFT) has gained widespread popularity, achieving good recognition rates. In this work, we propose to use Fisher Vector encoding of binary features for video face recognition, in order to speed up the computation time of the representation. The experimental evaluation was conducted on the challenging YouTube Faces database, showing that the proposed method is very efficient, and has an accuracy comparable with state-of-the-art methods.