The world is shifting to the digital era in an enormous pace. This rise in the digital technology has created plenty of applications in the digital space, which demands a secured environment for transacting and authenticating the genuineness of end users. Biometric systems and its applications has seen great potentials in its usability in the tech industries. Among various biometric traits, sclera trait is attracting researchers from experimenting and exploring its characteristics for recognition systems. This paper, which is first of its kind, explores the power of Convolution Neural Network (CNN) for sclera recognition by developing a neural model that trains its neural engine for a recognition system. To do so, the proposed work uses the standard benchmark dataset called Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset, which comprises of 734 images which are captured at different viewing angles from 30 different classes. The proposed methodology results showcases the potential of neural learning towards sclera recognition system.

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The culmination of technology-driven society has bought individuals in a lot of digital transactions. How legitimate these transactions are, is the question of the hour. Biometric-enabled transactions have gained popularity. Sclera, a new biometricbased recognition system promises to add value to such transactions. However, this recognition is purely based on the effective segmentation of the sclera from the occluded region of the eye. This work proposes a Modified Intuitionistic Fuzzy Clustering approach for the effective segmentation of sclera images. The traditional fuzzy set assumes that the non-membership value is always the complement of the membership value. But in the true sense, this assumption is not always correct because of hesitation. To alleviate the problems of hesitation degree and noise in the images, the Modified Intuitionistic Fuzzy C-Means (MIFCM) is proposed and tested against the Sclera Segmentation and Recognition Benchmarking Competition (SSRBC2016) and Sclera Segmentation Benchmarking Competition (SSBC 2019) dataset. The experimentation results reveal that the proposed work complements the other existing methods and variants of Fuzzy C-Means.