Reducing dense local feature key-points for faster iris recognition. (August 2018)

• Record Type: Journal Article
• Authors: Sahu, Beeren; Kumar Sa, Pankaj; Bakshi, Sambit; Sangaiah, Arun Kumar
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 939
• Page End: 949
• Publication Date: 2018-08
• Subjects: Iris recognition -- Local feature -- Phase intensive local pattern -- Density-based spatial clustering -- Feature reduction; DBSCAN Density-Based Spatial Clustering of Applications with Noise -- LDA Linear Discriminant Analysis -- NIR Near infrared -- PCA Principal Component Analysis -- PILP Phase Intensive Local Pattern -- ROC Receiver Operating Curve -- SIFT Scale Invariant Feature Transform -- SURF Speeded Up Robust Features; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.048
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Iris recognition has gained much attention in research and commercialization during the last decade. For a large population, the matching time of iris biometric system is much slower than the requirement. More the enrolled population size, higher the identification delay. To combat the delay without compromising accuracy of the system, the proposed method introduces a density-based spatial clustering and key point reduction to be applied on Phase Intensive Local Pattern (PILP) based dense feature extracted from the image. The reduction technique can also work with other dense local features. The reduction method is investigated whether it harms the accuracy of iris biometric system with respect to PILP. Widely used databases: BATH and CASIAv3 are used for experimentation. The technique is found successful in reducing representative key-points, thereby speeding up the match time up to five times. This improvement in 1:1 match-time is significant, and becomes more meaningful in identification for a large population.