134,334 research outputs found
Collaborative Representation based Classification for Face Recognition
By coding a query sample as a sparse linear combination of all training
samples and then classifying it by evaluating which class leads to the minimal
coding residual, sparse representation based classification (SRC) leads to
interesting results for robust face recognition. It is widely believed that the
l1- norm sparsity constraint on coding coefficients plays a key role in the
success of SRC, while its use of all training samples to collaboratively
represent the query sample is rather ignored. In this paper we discuss how SRC
works, and show that the collaborative representation mechanism used in SRC is
much more crucial to its success of face classification. The SRC is a special
case of collaborative representation based classification (CRC), which has
various instantiations by applying different norms to the coding residual and
coding coefficient. More specifically, the l1 or l2 norm characterization of
coding residual is related to the robustness of CRC to outlier facial pixels,
while the l1 or l2 norm characterization of coding coefficient is related to
the degree of discrimination of facial features. Extensive experiments were
conducted to verify the face recognition accuracy and efficiency of CRC with
different instantiations.Comment: It is a substantial revision of a previous conference paper (L.
Zhang, M. Yang, et al. "Sparse Representation or Collaborative
Representation: Which Helps Face Recognition?" in ICCV 2011
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods
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