A Novel Video Face Verification Algorithm Based on TPLBP and the 3D Siamese-CNN

In order to reduce the computational consumption of the training and the testing phases of video face recognition methods based on a global statistical method and a deep learning network, a novel video face verification algorithm based on a three-patch local binary pattern (TPLBP) and the 3D Siamese...

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Veröffentlicht in:Electronics (Basel) Jg. 8; H. 12; S. 1544
Hauptverfasser: Wang, Yu, Ma, Shuyang, Shen, Xuanjing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2019
Schlagworte:
Algorithms
Artificial intelligence
Artificial neural networks
Consumption
Deep learning
Face recognition
Feature extraction
Feature maps
Linear transformations
Machine learning
Neural networks
Statistical methods
Texture
Verification
ISSN:2079-9292, 2079-9292
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Zusammenfassung:In order to reduce the computational consumption of the training and the testing phases of video face recognition methods based on a global statistical method and a deep learning network, a novel video face verification algorithm based on a three-patch local binary pattern (TPLBP) and the 3D Siamese convolutional neural network is proposed in this paper. The proposed method takes the TPLBP texture feature which has excellent performance in face analysis as the input of the network. In order to extract the inter-frame information of the video, the texture feature maps of the multi-frames are stacked, and then a shallow Siamese 3D convolutional neural network is used to realize dimension reduction. The similarity of high-level features of the video pair is solved by the shallow Siamese 3D convolutional neural network, and then mapped to the interval of 0 to 1 by linear transformation. The classification result can be obtained with the threshold of 0.5. Through an experiment on the YouTube Face database, the proposed algorithm got higher accuracy with less computational consumption than baseline methods and deep learning methods.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8121544