Multispectral Face Recognition in Texture Space

A. Bendada, M. Akhloufi
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引用次数: 23

Abstract

This work introduces the use of LBP like texture descriptors for efficient multispectral face recognition. LBP has been widely used in visible spectrum face recognition. This work extend its use to non visible spectrums (active and passive infrared spectrums). Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) descriptors are used. Also a simple differential LTP descriptor (DLT) is introduced. The proposed texture space is less sensitive to noise, illumination change and facial expressions. These characteristics make it a good candidate for efficient multispectral face recognition. Linear and non linear dimensionality reduction techniques are introduced and used for performance evaluation of multispectral face recognition in the texture space. The obtained results show that the use of the proposed texture descriptors permit to achieve high recognition rates in multispectral face recognition.
纹理空间中的多光谱人脸识别
本文介绍了使用LBP类纹理描述符进行高效的多光谱人脸识别。LBP在可见光谱人脸识别中得到了广泛的应用。这项工作将其应用扩展到非可见光谱(主动和被动红外光谱)。使用了局部二进制模式(LBP)和局部三元模式(LTP)描述符。还介绍了一个简单的微分LTP描述符(DLT)。所提出的纹理空间对噪声、光照变化和面部表情的敏感性较低。这些特征使其成为高效的多光谱人脸识别的良好候选。介绍了线性和非线性降维技术,并将其用于纹理空间中多光谱人脸识别的性能评估。实验结果表明,本文提出的纹理描述符在多光谱人脸识别中具有较高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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