Open AccessFace and occlusion Recognition Algorithm based on Global and Local
Author(s) -
Guanwen Yu,
Zhaogong Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1453/1/012019
Subject(s) - softmax function , artificial intelligence , convolutional neural network , computer science , facial recognition system , occlusion , pattern recognition (psychology) , robustness (evolution) , feature extraction , artificial neural network , scale invariant feature transform , classifier (uml) , medicine , biochemistry , chemistry , cardiology , gene
Nowadays, the face recognition direction of unobstructed has been greatly developed, but there is still a huge deficiency in face recognition with occlusion. In order to improve the recognition rate under occlusion, this paper proposes a global and local occlusion recognition algorithm using deep learning for feature learning. Due to occlusion, local significant features will be missing, the impact will cause the accuracy of global recognition to decrease. Therefore, SIFT and SVM algorithms are used to discriminate partial face occlusion, and cut off the occluded local face to form a global face. Global facial features and unmasked local feature are extracted together. In the feature extraction, the LeNet-5 lightweight convolutional neural network and the improved LeNet-5 lightweight convolutional neural network are combined with SoftMax multi-classification. The final classification results use the improved hierarchical voting method to make final decisions on the face, which speeds up the training and prediction speed of the multi-classifier. This experiment will perform occlusion face verification on the AR dataset. The final experimental results show that the method greatly improves the accuracy of occlusion face recognition and has strong robustness to occlusion.