Affiliations: Computer Science and Engineering, VNIT, Nagpur, Maharashtra, India
Correspondence:
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Corresponding author. Jitendra Madarkar, Computer Science and Engineering, VNIT, Nagpur, Maharashtra, India, 440010. E-mail: jitendramadarkar475@gmail.com.
Abstract: Today’s world is facing threats from terrorism, for safety concerns system needs to strengthen security. Security is a challenging task and it can be strengthened by technology such as biometric and surveillance cameras. These technologies are deployed everywhere but it is the need of the days a strong automatic face recognition applications so they can be used to recognize the person in an unconstrained environment. In an unconstrained environment, images are affected by occlusion such as a scarf, goggle, random but these variations decrease the performance of face recognition. Also, the accuracy of face recognition depends on the number of labeled samples and variation available in the training dataset. But some applications of face recognition such as passport verification, identification of these applications have fewer training samples without or with very less occlusion hence, it is not enough to solve the issue of unconstrained conditions. This problem has been targeted by many researchers using an occlusion based training dataset where common variation exists in both training and testing datasets. This paper tackles the occlusion issues by designing a NonCoherent dictionary. The proposed dictionary is designed by two steps firstly it extracts the occlusion from the face image and secondly creates NonCoherent samples. The extensive experimentation is done on benchmark face databases and compared the results on state-of-the-art SRC methods by using NonCoherent and normal dictionary also compared the sparse coefficients of each method. The results show the effectiveness of proposed model.
Keywords: Face recognition, sparse representation, occlusion, dictionary
