Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
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DOI：
10.1007/978-3-540-75690-3_13
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发表时间：
2007-10
影响因子：
10.6
通讯作者：
Xiaoyang Tan;B. Triggs
中科院分区：
文献类型：
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作者：
Xiaoyang Tan;B. Triggs
非受控环境下的人脸识别是人脸识别系统实用化的重要瓶颈之一。我们解决这个问题，结合强大的照明归一化，局部纹理的人脸表示和距离变换的匹配度量的优势。具体来说，我们做了三个主要贡献：（i）我们提出了一个简单而有效的预处理链，消除了大部分的影响，改变照明，同时仍然保留必要的外观细节，需要识别;（ii）我们引入局部三值模式（LTP），局部二进制模式（LBP）局部纹理描述符的泛化，其更具鉴别力并且对均匀区域中的噪声不太敏感;和（iii）我们表明，取代本地直方图与本地距离变换为基础的相似性度量进一步提高了LBP/LTP的人脸识别的性能。由此产生的方法在三个流行的数据集上提供了最先进的性能，这些数据集被选择用于在困难的照明条件下测试识别：人脸识别大挑战第1版实验4，扩展的Yale-B和CMU PIE。
Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.