Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (8): 2210-2216.DOI: 10.11772/j.issn.1001-9081.2019010181

• Artificial intelligence • Previous Articles     Next Articles

Face recognition combining weighted information entropy with enhanced local binary pattern

DING Lianjing, LIU Guangshuai, LI Xurui, CHEN Xiaowen   

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China
  • Received:2019-01-25 Revised:2019-04-08 Online:2019-08-10 Published:2019-04-17
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (51275431), the Science and Technology Support Program of Sichuan (2015GZ0200).


丁莲静, 刘光帅, 李旭瑞, 陈晓文   

  1. 西南交通大学 机械工程学院, 成都 610031
  • 通讯作者: 刘光帅
  • 作者简介:丁莲静(1992-),男,贵州凤岗人,硕士研究生,主要研究方向:图像识别、机器视觉;刘光帅(1978-),男,贵州天柱人,副教授,博士,主要研究方向:逆向工程、图形图像处理;李旭瑞(1996-),男,山西曲沃人,硕士研究生,主要研究方向:模式识别、机器视觉;陈晓文(1995-),男,湖北应城人,硕士研究生,主要研究方向:模式识别、机器视觉。
  • 基金资助:

Abstract: Under the influence of illumination, pose, expression, occlusion and noise, the recognition rate of faces is excessively low, therefore a method combining weighted Information Entropy (IEw) with Adaptive-Threshold Ring Local Binary Pattern (ATRLBP) (IEwATR-LBP) was proposed. Firstly, the information entropy was extracted from the sub-blocks of the original face image, and then the IEw of each sub-block was obtained. Secondly, the probability histogram was obtained by using ATRLBP operator to extract the features of face sub-blocks. Finally, the final feature histogram of original face image was obtained by concatenating the multiplications of each IEw with the probability histogram, and the recognition result was calculated through Support Vector Machine (SVM). In the comparison experiments on the illumination, pose, expression and occlusion datasets from AR face database, the proposed method achieved recognition rates of 98.37%, 94.17%, 98.20%, and 99.34% respectively; meanwile, it also achieved the maximum recognition rate of 99.85% on ORL face database. And the average recognition rates in 5 experiments with different training samples were compared to conclude that the recognition rate of samples with Gauss noise was 14.04 percentage points lower than that of samples without noise, while the recognition rate of samples with salt & pepper noise was only 2.95 percentage points lower than that of samples without noise. Experimental results show that the proposed method can effectively improve the recognition rate of faces under the influence of illumination, pose, occlusion, expression and impulse noise.

Key words: face recognition, Local Binary Pattern (LBP), weighted information entropy, adaptive threshold, deep learning

摘要: 针对人脸识别因光照、姿态、表情、遮挡及噪声等多种因素的影响而导致的识别率不高的问题,提出一种加权信息熵(IEw)与自适应阈值环形局部二值模式(ATRLBP)算子相结合的人脸识别方法(IE (w) ATR-LBP)。首先,从原始人脸图像分块提取信息熵,得到每个子块的IEw;然后,利用ATRLBP算子分别对每个人脸子块提取特征从而得到概率直方图;最后,将各个块的IEw与概率直方图相乘,再串联成为原始人脸图像最后的特征直方图,并利用支持向量机(SVM)对人脸进行识别。在AR人脸库的表情、光照、遮挡A和遮挡B四个数据集上,IE (w) ATR-LBP方法分别取得了98.37%、94.17%、98.20%和99.34%的识别率。在ORL人脸库上,IE (w) ATR-LBP方法的最大识别率为99.85%;而且在ORL人脸库5次不同训练样本的实验中,与无噪声时相比,加入高斯和椒盐噪声后的平均识别率分别下降了14.04和2.95个百分点。实验结果表明,IE (w) ATR-LBP方法能够有效提高人脸在受光照、姿态、遮挡等影响时的识别率,尤其是存在表情变化及脉冲类噪声干扰时的识别率。

关键词: 人脸识别, 局部二值模式, 加权信息熵, 自适应阈值, 深度学习

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