3D Face Recognition Method Based on Deep Convolutional Neural Network
In 2D face recognition, result may suffer from the impact of varying pose, expression, and illumination conditions. However, 3D face recognition utilizes depth information to enhance systematic robustness. Thus, an improved deep convolutional neural network (DCNN) combined with softmax classifier to identify face is trained. First, the preprocessing of color image and depth map is different in removing redundant information. Then, the feature extraction networks for 2D face image and depth map are, respectively, build with the principle of recognition rate maximization, and parameters about neural networks reset by a series of tests, in order to acquire higher recognition rate. At last, the fusion of two feature layers is the final input of artificial neural network (ANN) recognition system, which is followed by a 64-way softmax output. Experimental results demonstrate that it is effective in improving recognition rate.
Keywords3D face recognition Depth map Deep convolutional neural network Feature extraction Feature fusion
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