An accurate and efficient face recognition method based on hash coding
Yan Zeng, Xiaodong Cai, Yuelin Chen, Meng Wang · 2017
To improve the efficiency in face recognition with highdimension features extracted from deep model, a fast recognition method based on hash coding is proposed. Different from others, the hash coding and the cascade network are designed for a two-stage face recognition. Firstly, the low-dimensional and high-dimensional features are extracted according to different models. Secondly, the low-dimensional features are quantized into hash codes by a piecewise function. And then, the first-identify is completed by calculating hamming distance between the hash codes. Finally, the second-identify is completed by calculating cosine distance between the high-dimensional features of face images after the first-identify. The experimental results show that the method proposed can improve the Rank-1 recognition efficiency up to 64% while the accuracy is the same as VGG.