10 April 2018 Face recognition via sparse representation of SIFT feature on hexagonal-sampling image
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150N (2018) https://doi.org/10.1117/12.2304894
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.
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Daming Zhang, Xueyong Zhang, Lu Li, Huayong Liu, "Face recognition via sparse representation of SIFT feature on hexagonal-sampling image", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150N (10 April 2018); doi: 10.1117/12.2304894; https://doi.org/10.1117/12.2304894
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