A robust multi-level sparse classifier with multi-modal feature extraction for face recognition
Gargi Mishra, Virendra Prasad Vishwakarma · International Journal of Applied Pattern Recognition · 2019
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.