Ms. MengLu Wu Profile

MengLu Wu

at Wuhan Institute of Technology

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Proceedings Article | 10 April 2018 Paper

Face recognition via Gabor and convolutional neural network

Tongwei Lu, Menglu Wu, Tao Lu

Proceedings Volume 10615, 106150V (2018) https://doi.org/10.1117/12.2304587

KEYWORDS: Databases, Autoregressive models, Convolution, Facial recognition systems, Convolutional neural networks, Detection and tracking algorithms, Neurons, Feature extraction, Wavelets, Neural networks

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In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.

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