Abstract
Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.
Highlights
The face recognition field always stayed an active topic in the computer vision research area
Our research study is aimed to improve the existing technique proposed by Yang et al [7], in which Local Binary Pattern (LBP) was used followed by multipartition max-pooling and Robust Kernel Representation (RKR)
The proposed technique provides much better results compared to the other state-of-the-art methods, including SLC-ADL [40], LRC [41], Sparse Representation-based Classification (SRC) [42], Collaborative Representation-based Classification (CRC) [27], ESRC [43], TPTSR [44], CLDA [45], LPP [46], CRC-ADL [8], Two-Step LSRC [47], RRC [48], RCR [49], Homotopy [50] and FISTA [51]
Summary
The face recognition field always stayed an active topic in the computer vision research area. Researchers have introduced many techniques that were used to increase the recognition accuracy with lesser time and lesser processing cost [1]. During recent years, facial recognition and other biometric verification systems have developed greatly. The dependency of protection on a single authorization method is out of the question [2]. Since the cyber threats and attacks are getting stronger, the security methods can be improved with the provision of introducing multiple factor authorization.
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