Improving Face Recognition Performance Using Tecs2 Dictionary

Human mind processes the different primitive components of image signals such as color, shape, texture, and symmetry in a parallel and complex fashion. Deep neural networks aim to learn all these components from the image in an unsupervised manner. However, learning the primitive features is not formally assured in a deep learning formulation, and, adding these features explicitly would improve the performance. Especially in face recognition, humans intuitively and implicitly employ the usage of primitive features such as color, shape, texture, and symmetry of faces. Inspired by this observation, this paper presents a novel approach in building a learning based TeCS2 space. This space consists of meta-level features obtained from dictionary learning and combining it with task specific deep learning classifiers (such as DenseNet) for face recognition. Confidence based fusion mechanism is presented to supplement the task specific deep learning classifier with the proposed TeCS2 features. The effectiveness of the proposed framework is evaluated on four benchmark face recognition datasets: (i) Disguised Faces in the Wild (DFW), (ii) Labeled faces in the wild (LFW), (iii) IIITD Plastic Surgery dataset, and (iv) Point and Shoot Challenge (PaSC). (C) 2021 Elsevier B.V. All rights reserved.