Inspired by how human being accomplishes face recognition task, a new architecture, called transfer and specialized net (TS-Net) is proposed in this paper, which fuses the general and specialized knowledge by combining a Transfer FaceNet and a Specialized FaceNet. The former is obtained by fine-tuning the pre-trained GoogleNet to transfer object-recognition knowledge to face recognition, and the latter is trained on global and local face patches to provide the discriminative specialized knowledge for face recognition. The final face representation is formed by fusing the features from both FaceNets. The advantages of our proposed architecture come from that: (i) By explicitly assigning different learning rates to different layers we successfully transfer the well-trained GoogleNet from object recognition task to a distinctly different task - face recognition; (ii) We construct the Specialized FaceNet with 6 simple networks to imitate the capture of featured-based and configural information in human vision process; (iii) Both Transfer FaceNet and Specialized FaceNets can be trained with a relatively small amount of training data (about 0.4 million samples) and a low configuration hardware (for example, a Titan-Z GPU). Experimental results show that TS-Net achieves competitive performance on both LFW and CASIA-Webface datasets. Also, it is promising that only slight dropping is found on verification and identification accuracy when 300 dimensional binary face representations are applied with Cosine distance as measure, which is implemental to develop practical human face retrieval and recognition system.
['Shilun Lin', 'Zhicheng Zhao', 'Fei Su']
Homemade TS-Net for Automatic Face Recognition