Robust periocular recognition by fusing local to holistic sparse representations

Authors:
Juan C. Moreno;V. B. Surya Prasath;Hugo Proença
Affiliations:
University of Beira Interior, Covilhã, Portugal;University of Missouri-Columbia, MO;University of Beira Interior, Covilhã, Portugal
Venue:
Proceedings of the 6th International Conference on Security of Information and Networks
Year:
2013

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Abstract

Sparse representations have been advocated as a relevant advance in biometrics research. In this paper we propose a new algorithm for fusion at the data level of sparse representations, each one obtained from image patches. The main novelties are two-fold: 1) a dictionary fusion scheme is formalised, using the l1--- minimization with the gradient projection method; 2) the proposed representation and classification method does not require the non-overlapping condition of image patches from where individual dictionaries are obtained. In the experiments, we focused in the recognition of periocular images and obtained independent dictionaries for the eye, eyebrow and skin regions, that were subsequently fused. Results obtained in the publicly available UBIRIS.v2 data set show consistent improvements in the recognition effectiveness when compared to state-of-the-art related representation and classification techniques.