Article: A robust multi-level sparse classifier with multi-modal feature extraction for face recognition Journal: International Journal of Applied Pattern Recognition (IJAPR) 2019 Vol.6 No.1 pp.76 - 102 Abstract: In the past few years, face recognition based on sparse representation is providing satisfactory classification accuracy. Face images involved in real life applications usually exhibit considerable pose, lighting, and expression variations, resulting in significant performance degradation of traditional sparse-based algorithms. In this paper, a novel face recognition method is developed as multi-level sparse (MLS) classifier with multi-modal feature extraction, which integrates benefits of sparse representation manifolds. In MLS classifier, sparse representation-based classification is performed at multiple levels to extract the hierarchical relationship information between training and testing images, which not only improves classification accuracy but also makes the system scalable. Also, the use of multi-modal feature in MLS classifier makes it discriminative to face changes while robust to intra-personal variations. To highlight the competency of proposed method, results are compared with sparse representation and other existing state-of-art methods in terms of mean classification error. An investigation on classification accuracy is performed to showcase the reliability of proposed method. Inderscience Publishers - linking academia, business and industry through research

Title: A robust multi-level sparse classifier with multi-modal feature extraction for face recognition

Authors: Virendra P. Vishwakarma; Gargi Mishra

Addresses: University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, New Delhi, India ' University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

Abstract: In the past few years, face recognition based on sparse representation is providing satisfactory classification accuracy. Face images involved in real life applications usually exhibit considerable pose, lighting, and expression variations, resulting in significant performance degradation of traditional sparse-based algorithms. In this paper, a novel face recognition method is developed as multi-level sparse (MLS) classifier with multi-modal feature extraction, which integrates benefits of sparse representation manifolds. In MLS classifier, sparse representation-based classification is performed at multiple levels to extract the hierarchical relationship information between training and testing images, which not only improves classification accuracy but also makes the system scalable. Also, the use of multi-modal feature in MLS classifier makes it discriminative to face changes while robust to intra-personal variations. To highlight the competency of proposed method, results are compared with sparse representation and other existing state-of-art methods in terms of mean classification error. An investigation on classification accuracy is performed to showcase the reliability of proposed method.

Keywords: multi-level sparse; MLS classifier; face recognition; sparse representation; pattern recognition; nearest neighbours.

DOI: 10.1504/IJAPR.2019.104300

International Journal of Applied Pattern Recognition, 2019 Vol.6 No.1, pp.76 - 102

Received: 10 Jun 2019
Accepted: 31 Jul 2019
Published online: 02 Jan 2020 *

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Title: A robust multi-level sparse classifier with multi-modal feature extraction for face recognition

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