A Deep Learning Face Recognition Algorithm Based on Local Ternary Pattern

doi:10.3969/j.issn.1006-2475.2018.02.023

Abstract

Abstract: In order to solve the problems of the heavy dependence on artificial selection during the process of traditional feature extraction and leaving local features out of consideration in traditional DBN network， this paper proposes a face recognition algorithm based on local ternary pattern and deep learning (LTDBN) to get higher face recognition ratio. This algorithm firstly segments a normalized face image into multiple small parts equally and carries out LTP algorithm for each part. Then the histogram is used to get the final image features. These image features are served as the input data of DBN. The greedy learning algorithm trains and recognizes the whole network level by level. The recognition ratio reaches 98.75%， 100% and 96.62% respectively in public face databases including ORL， Yale and Yale-B. The experiment results indicate that LTDBN algorithm is markedly superior to other existing algorithms in recognition ratio and mitigates the negative effects of factors such as illumination and posture.

Key words: LTP, face recognition, deep learning, DBN

CLC Number:

TP391.4

ZHENG Qiu-mei， XIE Huan-li， WANG Feng-hua， SU Zheng， LIU Zhen. A Deep Learning Face Recognition Algorithm Based on Local Ternary Pattern[J]. Computer and Modernization, 2018, 0(02): 112-.

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