Virtual Image Representation and Adaptive Weighted Score Level Fusion for Genetic Face Recognition

Face recognition using sparse representation-based classification (SRC) is a new hot technique in recent years. However, the research indicates that it is the collaborative representation but not the [Formula: see text]-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a simple yet much more efficient face classification scheme, namely two-step collaborative representation-based classification (TSCRC) method. First, we exploit the symmetry of the face to generate new images of each test sample. Then, the original and new generated test samples are, respectively, used to perform TSCRC, which ultimately uses a small number of classes that are near to the test sample to represent and classify it. Finally, the score level fusion is taken to perform classification recognition. The experimental results clearly show that the proposed method has very competitive classification results.

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Expression invariant face recognition using semidecimated DWT, Patch-LDSMT, feature and score level fusion

Applied Intelligence ◽

10.1007/s10489-015-0735-1 ◽

2015 ◽

Vol 44 (4) ◽

pp. 913-930 ◽

Cited By ~ 5

Author(s):

Hemprasad Patil ◽

Ashwin Kothari ◽

Kishor Bhurchandi

Keyword(s):

Face Recognition ◽

Score Level Fusion ◽

Level Fusion

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Score level fusion in representation-based classification method for face recognition

Journal of Algorithms & Computational Technology ◽

10.1177/1748302620930943 ◽

2020 ◽

Vol 14 ◽

pp. 174830262093094

Author(s):

Zi-Qi Li ◽

Jun Sun ◽

Xiao-Jun Wu ◽

He-Feng Yin

Keyword(s):

Face Recognition ◽

Least Square ◽

Classification Method ◽

Collaborative Representation ◽

Score Level Fusion ◽

Training Samples ◽

Global Representation ◽

Face Datasets ◽

Representation Method ◽

Level Fusion

Recent years have witnessed the success of representation-based classification method (RBCM) in the domain of face recognition. Collaborative representation-based classification (CRC) and linear regression-based classification (LRC) are two representative approaches. CRC is a global representation method which uses all training samples to represent test samples and utilizes representation residuals to perform classification, whereas LRC is a local representation method which exploits training samples from each class to represent test samples. Related researches indicate that the combination of LRC and CRC can fully exploit the representation residuals produced by them, thus improving the performance of RBCM. However, the representation coefficients obtained by CRC usually contain negative values which may result in overfitting problem. Therefore, to solve this problem to some extent, the combination of LRC and non-negative least square-based classification (NNLSC) is proposed in this paper. Experimental results on benchmark face datasets show that the proposed method is superior to the combination of LRC and CRC and other state-of-the-art RBCMs. The source code of our proposed method is available at https://github.com/li-zi-qi/score-level-fusion-of-NNLS-and-LRC .

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