Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition
نویسندگان
چکیده
منابع مشابه
Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition
Face recognition has been popular in pattern recognition field for decades, but it is still a difficult problem due to the various image distortions. Recently, Sparse Representation based Classification (SRC) was proposed as a novel image classification approach, which is very effective with sufficient training samples for each class. However, the performance drops when the number of training s...
متن کاملSupplementary Materials: Kernel Sparse Representation with Pixel-level and Region-level Local Feature Kernels For Face Recognition
We compared the proposed KCDSRC algorithm with the KMTJSRC algorithm [3] on the Extended YaleB and the CMU-PIE databases. The proposed LBPh-KH kernel is used due to its best overall performance than the other kernels. The experiment is conducted under three conditions, illumination, random noise, and synthesized continuous occlusion, where the settings are the same as we have applied before. Th...
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Finger-vein is a promising biometric technique for the identity authentication. However, the finger displacement or the illumination variation in image capturing may cause bad recognition performance. To overcome these limitations, multi-biometric system, an effective method to improve the performance, is proposed. In this paper, a new multimodal biometric system based on pixel level feature an...
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Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may reduce the feature quantization error and boost the sparse coding performance, we propose Kernel Sparse Representation(KSR). KSR is essentially the sparse coding technique in a high dime...
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Kernel sparse representation for classification (KSRC) has attracted much attention in pattern recognition community in recent years. Although it has been widely used in many applications such as face recognition, KSRC still has some open problems needed to be addressed. One is that if the training set is of a small scale, KSRC may potentially suffer from lack of training samples when a nonline...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2014
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2013.11.022