Fast segmentation and adaptive SURF descriptor for iris recognition

• Published in: Mathematical and computer modelling Vol. 58; no. 1-2; pp. 132 - 146
• Main Authors: Mehrotra, Hunny, Sa, Pankaj K., Majhi, Banshidhar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2013
• Subjects: Adaptive normalization; Adaptive thresholding; computer techniques; decision making; Gamma enhancement; Hole filling; Iris biometrics; lighting; mathematical models; SURF; Iris biometrics; Hole filling; SURF; Adaptive normalization; Gamma enhancement; Adaptive thresholding
• ISSN: 0895-7177; 1872-9479
• DOI: 10.1016/j.mcm.2012.06.034

In this paper a robust segmentation and an adaptive SURF descriptor are proposed for iris recognition. Conventional recognition systems extract global features from the iris. However, global features are subject to change for transformation, occlusion and non-uniform illumination. The proposed iris recognition system handles these issues. The input iris image is used to remove specular highlights using an adaptive threshold. Further, the pupil and iris boundaries are localized using a spectrum image based approach. The annular region between the pupil and iris boundaries is transformed into an adaptive strip. The strip is enhanced using a gamma correction approach. Features are extracted from the adaptive strip using Speeded Up Robust Features (SURF). The results obtained using SURF are compared with the existing SIFT descriptor and the proposed approach performs with improved accuracy and reduced computation cost.