Feature extraction based ondeep-convolutional neural network for face recognition

Feature extraction is a critical technology that affects the accuracy of face recognition. However, certain features are highly related to changes in face are difficult to extract because of the influences of individual differences and illumination. Therefore, features can accurately describe the changes in face are urgently required. For this reason, this article proposes a feature extraction method based on deep learning. This method combines the features extracted by Local Binary Patterns and by Convolutional Neural Network convolutional layer in the network connection layer, thus obtaining classification features with high representation ability and solving the problem of single feature extraction. The VGG-16 network proposed in this article has been improved by changing the framework structure. Some experiments based on the Labeled Faces in the Wild dataset are performed, and results show that, in terms of accuracy and the sensitivity to light, the proposed method reaches 99.56% and 80.35% respectively. The recognition results obtained from fused features are superior to which of single feature recognition. Simulation results show that the method is more robust to changes in the illumination condition and more efficient than the existing methods.