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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Robust Kernel Representation With Statistical Local Features for Face Recognition
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Robust Kernel Representation With Statistical Local Features for Face Recognition

机译：具有局部统计特征的鲁棒内核表示

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摘要

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.
机译：诸如未对准，姿势变化和遮挡等因素使稳固的面部识别成为一个难题。众所周知，诸如局部二进制模式之类的统计特征对于局部特征提取是有效的，而最近提出的基于稀疏或基于协作表示的分类已在鲁棒的人脸识别中显示出有趣的结果。在本文中，我们提出了一种具有统计局部特征（SLF）的新型鲁棒内核表示模型，用于鲁棒的人脸识别。最初，多分区最大池用于增强SLF对图像配准误差的不变性。然后，提出了一种基于核的表示模型，以充分利用嵌入在SLF中的识别信息，并采用鲁棒回归有效地处理人脸图像中的遮挡。在基准人脸数据库上进行了广泛的实验，包括扩展的Yale B，AR（A. Martinez和R. Benavente），多重姿势，照明和表情（multi-PIE），面部识别技术（FERET），面部识别大挑战（ FRGC）和带有标签的野外面部（LFW），它们在光照，表情，姿势和遮挡方面有不同的变化，证明了所提出方法的良好前景。

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