106,269 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
Sparse representation for pose invariant face recognition
Face recognition is easily affected by pose angle. In order to improve the obustness to pose angle, we need to solve the pose estimation, face synthesis and recognition problem. Sparse representation can represent a face image with linear combination of atom faces. In this paper, we construct different pose dictionaries using face images captured under the same pose angle to estimate pose angle and synthesize front face images for recognition. Experimental results show that sparse representation can estimate pose angle accurately, synthesize near frontal faces very well and significantly improve the recognition rate for large pose angles
Innovative sparse representation algorithms for robust face recognition
In this paper, we propose two innovative and computationally efficient algorithms for robust face recognition, which extend the previous Sparse Representation-based Classification (SRC) algorithm proposed by Wright et al. (2009). The two new algorithms, which are designed for both batch and online modes, operate on matrix representation of images, as opposed to vector representation in SRC, to achieve efficiency whilst maintaining the recognition performance. We first show that, by introducing a matrix representation of images, the size of the ℓ1-norm problem in SRC is reduced fromO(whN) to O(rN), where r ≪ wh and thus higher computational efficiency can be obtained. We then show that the computational efficiency can be even enhanced with an online setting where the training images arrive incrementally by exploiting the interlacing property of eigenvalues in the inner product matrix. Finally, we demonstrate the superior computational efficiency and robust performance of the proposed algorithms in both batch and online modes, as compared with the original SRC algorithm through numerous experimental studies
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