[1]YANG Huixian,LIU Jian,ZHANG Mengjuan,et al.Face recognition with double difference local directional pattern[J].CAAI Transactions on Intelligent Systems,2018,13(5):751-759.[doi:10.11992/tis.201706032]
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Face recognition with double difference local directional pattern

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 5 Page number: 751-759 Column: 学术论文—机器感知与模式识别 Public date: 2018-09-05
Title:
Face recognition with double difference local directional pattern
Author(s):
YANG Huixian1;  LIU Jian1;  ZHANG Mengjuan1;  ZHOU Tongtong2
1. College of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China;
2. Mechanical and Electrical Engineering College, Hu’nan Institute of Applied Technology, Changde 415000, China
Keywords:
difference local directional pattern; feature extraction; double difference local directional pattern; face recognition; Kirsch operator; face features
CLC:
TP391.41
DOI:
10.11992/tis.201706032
Abstract:
To solve the problem of insufficient feature extraction and sensitivity to the noise and illumination encountered using difference local directional pattern (DLDP) method, this article proposes a double difference local direction pattern (DDLDP) face feature extraction method. First, a 3×3 domain pixel gray value with a radius of 1 and a 5×5 domain pixel gray value with a radius of 2 were convolved with eight Kirsch template operators to obtain two groups of eight gray response values. Then, the gray-scale response value with a radius of 1 was obtained as a difference between the values of the neighboring pixels at both sides to obtain eight gray-scale response differences. Meanwhile, the edge response difference values of different radius were also calculated. Finally, the two sets of gray response differences were taken as absolute values, and their maximum absolute values correspond to the subscripts form DDLDP code. Simulation experiment results show that the proposed algorithm has better recognition effect than other single face recognition algorithms based on local directional pattern (LDP). The DDLDP algorithm can fully extract the facial features, and have better robustness to illumination and noise.
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