The rising challenges in the fields of iris and face recognition are leading to a renewed interest in the area. In recent years the focus of research has turned towards alternative traits to aid in the recognition process under less constrained image acquisition conditions. The present work assesses the potential of the periocular region as an alternative to both iris and face in such scenarios. An automatic modeling of SIFT descriptors, regardless of the number of detected keypoints and using a GMM-based Universal Background Model method, is proposed. This framework is based on the Universal Background Model strategy, first proposed for speaker verification, extrapolated into an image-based application. Such approach allows a tight coupling between individual models and a robust likelihood-ratio decision step. The algorithm was tested on the UBIRIS.v2 and the MobBIO databases and presented state-of-the-art performance for a variety of experimental setups.
The use of images acquired in unconstrained scenarios is giving rise to new challenges in the field of iris recognition. Many works in literature reported excellent results in both iris segmentation and recognition but mostly with images acquired in controlled conditions. The intention to broaden the field of application of iris recognition, such as airport security or personal identification in mobile devices, is therefore hindered by the inherent unconstrained nature under which images are to be acquired. The proposed work focuses on mutual context information from iris centre and iris limbic and pupillary contours to perform robust and accurate iris segmentation in noisy images. The developed algorithm was tested on the MobBIO database with a promising 96% segmentation accuracy for the limbic contour.
Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios.
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