EffectFace: A Fast and Efficient Deep Neural Network Model for Face Recognition

Abstract

Despite the Deep Neural Network (DNN) has achieved a great success in image recognition, the resource needed by DNN applications is still too much in terms of both memory usage and computing time, which makes it barely possible to deploy a whole DNN system on resource-limited devices such as smartphones and small embedded systems. In this paper, we present a DNN model named EffectFace designed for higher storage and computation efficiency without compromising the accuracy. EffectFace includes two sub-modules, EffectDet for face detection and EffectApp for face recognition. In EffectDet we use sparse and small-scale convolution cores (filters) to reduce the number of weights for less memory usage. In EffectApp, we use pruning and weights-sharing technology to further reduce weights. At the output stage of the network, we use a new loss function rather than the traditional Softmax function to acquire feature vectors of the input face images, which reduces the dimension of the output of the network from n to fixed 128 where n equals to the number of categories to classify. Experiments show that, compared with previous models, the amounts of weights of our EffectFace is dramatically decreased (less than 10% of previous models) without losing the accuracy of recognition.