Sparse Representation for Video-Based Face Recognition
Sparse Representation for Video-Based Face Recognition
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DOI：
10.1007/978-3-642-01793-3_23
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
I. Naseem;R. Togneri;Bennamoun
中科院分区：
文献类型：
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作者：
I. Naseem;R. Togneri;Bennamoun
在本文中，我们第一次解决的问题，基于视频的人脸识别的背景下，稀疏表示分类（SRC）。基于静态人脸图像的SRC分类方法是近年来基于视点的人脸识别研究中的一个新范式。在这项研究中，我们扩展了SRC算法的时间人脸识别问题。使用VidTIMIT数据库进行了广泛的识别和验证实验[1，2]。与最先进的基于尺度不变特征变换（SIFT）的识别进行了比较分析。SRC算法实现了94.45%的识别准确率，这与基于SIFT的方法的93.83%的结果相当。验证实验产生了1.30%的等效错误率（EER）的SRC的表现优于SIFT方法的0.5%的利润率。最后采用加权求和规则对两个分类器进行融合。融合结果始终优于识别，验证和等级配置文件评估协议的个别专家。
In this paper we address for the first time, the problem of video-based face recognition in the context of sparse representation classification (SRC). The SRC classification using still face images, has recently emerged as a new paradigm in the research of view-based face recognition. In this research we extend the SRC algorithm for the problem of temporal face recognition. Extensive identification and verification experiments were conducted using the VidTIMIT database [1,2]. Comparative analysis with state-of-the-art Scale Invariant Feature Transform (SIFT) based recognition was also performed. The SRC algorithm achieved 94.45% recognition accuracy which was found comparable to 93.83% results for the SIFT based approach. Verification experiments yielded 1.30% Equal Error Rate (EER) for the SRC which outperformed the SIFT approach by a margin of 0.5%. Finally the two classifiers were fused using the weighted sum rule. The fusion results consistently outperformed the individual experts for identification, verification and rank-profile evaluation protocols.