Face Recognition in Complex Environment Based on Adaptive Feature Extraction Network
Aiming at the problem that existing face recognition algorithms have low recognition rates and poor robustness in real and complex environments such as motion blur and low light,which makes it difficult to be stably applied in actual face recognition tasks,a face recognition method in complex environments based on adaptive feature extraction network is proposed.The network combines the feature extraction technology of traditional methods with the feature representation ability of deep learning network,and realizes the stable face recognition in different complex environments.An adaptive texture feature extraction algorithm is designed,which realizes the feature extraction by automatically obtaining the threshold value and improves the network computing efficiency.The backpropagation algorithm is used to improve the deep belief network,and the conjugate gradient algorithm is introduced to solve the gradient disappearance of the network,which reduces its convergence time and improves the algorithm's robustness.Experimental results show that the accuracy of the proposed method reaches 99.72％,89.54％and 88.75％on standard LWF dataset,CASIA and MS1M datasets in complex environments,respectively.The parameter quantity and network computation are 2.84 M and 0.67 G,re-spectively,the proposed method is superior to the comparison algorithm,and can meet the needs of face recognition in complex envi-ronments.
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