Multi-patch deep sparse histograms for iris recognition in visible spectrum using collaborative subspace for robust verification

• Published in: Pattern recognition letters Vol. 91; pp. 27 - 36
• Main Authors: Raja, Kiran B., Raghavendra, R., Venkatesh, Sushma, Busch, Christoph
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2017; Elsevier Science Ltd
• Subjects: Biometric recognition systems; Biometrics; Collaborative subspace; Deep sparse filtering; Distance measurement; Feature extraction; Feature recognition; Histograms; Iris; Object recognition; Pattern recognition; Robustness; Single sample iris; Smartphone iris recognition; Smartphones; Visible iris recognition; Visible spectrum; Deep sparse filtering; Single sample iris; Visible iris recognition; Collaborative subspace; Smartphone iris recognition
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2016.12.025

•Multi-patch based and hoslistic image deep sparse features for improved iris recognition using collaborative subspace.•Explores color channel along with patch based approach for better feature representation.•Extensive analysis & results presented for both MICHE-I & MICHE-II databases.•High verification accuracy (MICHE-I & II) with single sample iris enrolment. The challenge of recognizing iris in visible spectrum images captured using smartphone stems from heavily degraded data (due to reflection, partial closure of eyes, pupil dilation due to light) where the iris texture is either not visible or visible to very low extent. In order to perform reliable verification, the set of extracted features should be robust and unique to obtain high similarity scores between different samples of same subject while obtaining high dissimilarity score between samples of different subjects. In this work, we propose multi-patch deep features using deep sparse filters to obtain robust features for reliable iris recognition. Further, we also propose to represent them in a collaborative subspace to perform classification via maximized likelihood, even under single sample enrolment. Through the set of extensive experiments on MICHE-I iris dataset, we demonstrate the robustness of newly proposed scheme which achieves high verification rate (GMR > 95%) with low Equal Error Rate (EER < 2%). Further, the robustness of proposed feature representation is reiterated by employing simple distance measures which has outperformed the state-of-art techniques. Additionally, the scheme is tested on the MICHE-II challenge evaluation dataset where the results are promising with GMR=100% on limited sub-corpus of iPhone data.