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.
丁莲静, 刘光帅, 李旭瑞, 陈晓文. 加权信息熵与增强局部二值模式结合的人脸识别[J]. 计算机应用, 2019, 39(8): 2210-2216.
DING Lianjing, LIU Guangshuai, LI Xurui, CHEN Xiaowen. Face recognition combining weighted information entropy with enhanced local binary pattern. Journal of Computer Applications, 2019, 39(8): 2210-2216.
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