SRDANet: An Efficient Deep Learning Algorithm for Face Analysis
In this work, we take advantage of the superiority of Spectral Graph Theory in classification application and propose a novel deep learning framework for face analysis which is called Spectral Regression Discriminant Analysis Network SRDANet. Our SRDANet model shares the same basic architecture of Convolutional Neural Network CNN, which comprises three basic components: convolutional filter layer, nonlinear processing layer and feature pooling layer. While it is different from traditional deep learning network that in our convolutional layer, we extract the leading eigenvectors from patches in facial image which are used as filter kernels instead of randomly initializing kernels and update them by stochastic gradient descent SGD. And the output of all cascaded convolutional filter layers is used as the input of nonlinear processing layer. In the following nonlinear processing layer, we use hashing method for nonlinear processing. In feature pooling layer, the block based histograms are employed to pooling output features instead of max pooling technique. At last, the output of feature pooling layer is considered as one final feature output of our model. Different from the previous single task research for face analysis, our proposed approach demonstrates an excellent performance in face recognition and expression recognition with 2D/3D facial images simultaneously. Extensive experiments conducted on many different face analysis databases demonstrate the efficiency of our proposed SRDANet model. Databases such as Extended Yale B, PIE, ORL are used for 2D face recognition, FRGC v2 is used for 3D face recognition and BU 3DFE is used for 3D expression recognition.
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