A Face Verification System on Mobile Terminal Based on Depth Learning

doi:10.3969/j.issn.1006-2475.2018.02.022

Abstract

Abstract: As a new authentication technology, face verification is widely used in access control, attendance and other needs of the occasion of authentication. This paper designs and implements a face verification system based on the Android system platform, taking into account the requirements and production environment of mobile face verification and the efficiency and portability of existing face verification algorithms. The system can be deployed off-line in the mobile device equipped with Android system, through the camera obtaining a face image and the local image processing data to complete face verification work. In the algorithm, the system uses deep convolution neural network for image processing and face feature vector extraction to improve the accuracy of face verification. In the implementation, through the joint compiler Java and C+〖KG-*3〗+ codes improving the efficiency of the algorithm to adapt to the depth learning algorithms in the mobile side of the application. Experiments show that the system can quickly ensure the accuracy rate of 97.16% under the premise of rapid completion of the face verification process, basically meet the needs of industrial applications.

Key words: face verification, depth learning, feature extraction, mobile terminal, off-line deployment

CLC Number:

TP391

LIU Cheng1, TAN Xiao-yang1,2. A Face Verification System on Mobile Terminal Based on Depth Learning[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.02.022.

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