一种煤矿井下复杂光照条件下的人脸识别方法
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摘要
为了解决煤矿井下复杂光照条件导致人脸识别率低的问题,提出了一种适用于煤矿井下复杂光照条件下的人脸识别方法。首先利用小波分解将人脸图像分解为低频和高频部分,对低频部分利用直方图均衡化处理,增强图像对比度;然后采用引入模糊隶属度因子的小波去噪模型对高频部分进行滤波处理,并通过新的PAL模糊增强算法对高频部分进行模糊增强,在不同阈值下的非线性变换得到不同尺度、不同方向的特征图像,并进行反模糊处理;最后对处理后的低频和高频部分进行小波重构。实验表明,在井下复杂光照条件下,本文提出的人脸识别方法能有效改善人脸图像的整体效果,增强图像的细节信息,且平均识别率能够达到94.45%,显著提高了井下复杂光照下的人脸识别率。
In order to solve the problem of low face recognition rate caused by the complex lighting conditions in coal mines,a face recognition method applied to the underground coal mines with complex lighting conditions is proposed.First,the face image is decomposed into low-frequency and high-frequency components by wavelet decomposition,and simultaneously the histogram equalization processing is conducted on the low-frequency components to enhance the image contrast.Then,the wavelet denoising model with a fuzzy degree of membership factor is used to filter the high-frequency components and meanwhile a new PAL fuzzy enhancement algorithm is adopted for the fuzzy enhancement of high-frequency components.Under different thresholds,a non-linear transformation is used to get feature images with different scales and different directions,and the anti-fuzzy processing is conducted.Finally,the processed low-frequency and high-frequency components are reconstructed based on wavelets.The experimental results show that the proposed face recognition method can be used to effectively improve the overall effect of face images and enhance the detail information of images under the complex lighting conditions in underground coal mines.Moreover,the average recognition rate can reach 94.45%,indicating the face recognition rate under complex lighting conditions in coal mines is significantly enhanced.
In order to solve the problem of low face recognition rate caused by the complex lighting conditions in coal mines,a face recognition method applied to the underground coal mines with complex lighting conditions is proposed.First,the face image is decomposed into low-frequency and high-frequency components by wavelet decomposition,and simultaneously the histogram equalization processing is conducted on the low-frequency components to enhance the image contrast.Then,the wavelet denoising model with a fuzzy degree of membership factor is used to filter the high-frequency components and meanwhile a new PAL fuzzy enhancement algorithm is adopted for the fuzzy enhancement of high-frequency components.Under different thresholds,a non-linear transformation is used to get feature images with different scales and different directions,and the anti-fuzzy processing is conducted.Finally,the processed low-frequency and high-frequency components are reconstructed based on wavelets.The experimental results show that the proposed face recognition method can be used to effectively improve the overall effect of face images and enhance the detail information of images under the complex lighting conditions in underground coal mines.Moreover,the average recognition rate can reach 94.45%,indicating the face recognition rate under complex lighting conditions in coal mines is significantly enhanced.
引文
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[2]Yang A P,Wang N.Nighttime image dehazing algorithm by structure-texture image decomposition[J].Laser&Optoelectronics Progress,2018,55(6):061001.杨爱萍,王南.基于结构-纹理分层的夜间图像去雾算法[J].激光与光电子学进展,2018,55(6):061001.
[3]Yang X Y,Deng X M.Identification method of ALBP and its application in aid system for deaf vision[J].Journal of Chongqing University of Technology(Natural Science),2015,29(8):94-98.杨小义,邓新梅.ALBP识别方法及其在聋人视觉辅助系统中的应用[J].重庆理工大学学报(自然科学),2015,29(8):94-98.
[4]Kong R,Zhang B.Research on face recognition method under uncontrolled illumination variation[J].Journal of System Simulation,2016,28(3):689-695.孔锐,张冰.光照变化条件下人脸识别方法研究[J].系统仿真学报,2016,28(3):689-695.
[5]Cheng X F,Li S,Long F.Illumination invariant face recognition based on Log-Gabor filtering and LBPdescriptor[J].Journal of Xiamen University(Natural Science),2014,53(3):359-363.程雪峰,李顺,龙飞.基于Log-Gabor滤波和LBP算子的光照不变人脸识别方法[J].厦门大学学报(自然科学版),2014,53(3):359-363.
[6]Bae C,Chung Y Y,Lee J.Image based video querying algorithm using 3-level Haar wavelet transform features[C]∥International Conference on Computer Science and Its Applications,2016:779-785.
[7]Yuan Y Z.Fuzzy membership degree threshold denoising algorithm of mine remote sensing image in wavelet domain[J].Metal Mine,2017(4):123-126.袁玉珠.矿山遥感图像小波域模糊隶属度阈值去噪算法[J].金属矿山,2017(4):123-126.
[8]Ding C,Dong L L,Xu W H.Enhancement technique for infrared scene with maritime target[J].Acta Optica Sinica,2018,38(6):0610001.丁畅,董丽丽,许文海.海面目标的红外景象增强技术研究[J].光学学报,2018,38(6):0610001.
[9]Huang T,Xue F C,Qian H L,et al.Remote sensing image denoising algorithm based on NSCTand adaptive fuzzy threshold[J].Computer Technology and Development,2016,26(1):65-69.黄涛,薛丰昌,钱洪亮,等.基于NSCT和自适应模糊阈值遥感图像去噪算法[J].计算机技术与发展,2016,26(1):65-69.
[10]Wang Y,Pan Z B.Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization[J].Infrared Physics&Technology,2017,86:59-65.
[11]Cheng D Q,Gao L Z,Chen L L,et al.Adaptive multi-scale block compressed sensing algorithm[J].Journal of Image and Graphics,2017,22(9):1175-1182.程德强,高凌志,陈亮亮,等.自适应多尺度分块压缩感知算法[J].中国图象图形学报,2017,22(9):1175-1182.
[12]Huang P F,Cai J,Chen L,et al.Fusion LBP and gray feature description based straight line segment matching method:105405147A[P].2016-03-16.黄攀峰,蔡佳,陈路,等.一种基于融合LBP和灰度特征描述的直线段匹配方法:105405147A[P].2016-03-16.
[13]Lian Z C,Er M J,Li J K.A novel face recognition approach under illumination variations based on local binary pattern[C]∥International Conference on Computer Analysis of Images and Patterns,2011:89-96.
[14]Huang J J,Chen W J,Su X Y,et al.Application of wavelet transform in modulation measurement profilometry[J].Acta Optica Sinica,2016,36(7):0707001.黄静静,陈文静,苏显渝,等.小波变换在调制度测量轮廓术中的应用[J].光学学报,2016,36(7):0707001.
[15]Liu X Y,Qiao T,Qiao Z.Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J].Industry and Mine Automation,2017,43(2):49-54.刘晓阳,乔通,乔智.基于双边滤波和Retinex算法的矿井图像增强方法[J].工矿自动化,2017,43(2):49-54.
[16]Li Q Z,Liu Q.Adaptive enhancement algorithm for low illumination images based on wavelet transform[J].Chinese Journal of Lasers,2015,42(2):0209001.李庆忠,刘清.基于小波变换的低照度图像自适应增强算法[J].中国激光,2015,42(2):0209001.
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