激光与光电子学进展, 2019, 56 (2): 023001, 网络出版: 2019-08-01  

基于热红外光谱的人脸特征提取算法 下载: 1161次

Extraction Algorithm of Face Features Based on Thermal Infrared Spectra
作者单位
河南工程学院电气信息工程学院, 河南 郑州 451191
摘要
基于可见光谱人脸识别技术的效率和精度受光照、姿态、遮挡、表情变化和照片欺诈等因素的影响较大,尤其是夜视环境下的人脸识别难题亟待解决。为此,提出了一种基于热红外光谱的人脸特征提取算法。对热红外人脸图像进行数据建模获取极大化数据模型,估计并调整混合模型参数到高斯混合模型;提取热红外高斯混合人脸图像的等温特征,实现热特征图像的重建;最后通过计算概率邻近指数来度量个体间的相似度,给出识别结果。UCHThermalFace数据库的实验结果表明:该方法应对夜视环境下的多姿态、特征变化、随机遮挡和眼部噪声样本具有较高的识别精度和稳健性,极大地提高了人脸识别系统夜视环境下的抗干扰能力。
Abstract
At present, the efficiency and accuracy of face recognition based on visible spectra are strongly influenced by the factors such as lighting, pose, occlusion, expression change and photo fraud, especially the face recognition problems in the night vision environments need to be solved. A face feature extraction algorithm based on thermal infrared spectra is proposed. The thermal infrared face images are modeled to obtain a maximal data model, and the mixed model parameters are estimated and adjusted as a Gaussian mixture model. The isothermal features of thermal infrared Gaussian mixture face images are extracted, and the thermal feature images are reconstructed. The similarity between individuals measured by the calculation of probabilistic proximity index is used to present the recognition results. The experimental results based on the UCHThermalFace database show that the proposed method has relatively high recognition precision and robustness to multiple attitudes, feature changes, random occlusion and eye noise samples, which greatly improves the anti-interference ability of face recognition system in night vision environments.

栗科峰, 黄全振, 卢金燕. 基于热红外光谱的人脸特征提取算法[J]. 激光与光电子学进展, 2019, 56(2): 023001. Kefeng Li, Quanzhen Huang, Jinyan Lu. Extraction Algorithm of Face Features Based on Thermal Infrared Spectra[J]. Laser & Optoelectronics Progress, 2019, 56(2): 023001.

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