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We address the fundamental performance issues of template protection (TP) for iris verification. We base our work on the popular Bloom-Filter templates protection & address the key challenges like sub-optimal performance and low unlinkability. Specifically, we focus on cases where Bloom-filter templates results in non-ideal performance due to presence of large degradations within iris images. Iris recognition is challenged with number of occluding factors such as presence of eye-lashes within captured image, occlusion due to eyelids, low quality iris images due to motion blur. All of such degrading factors result in obtaining non-reliable iris codes & thereby provide non-ideal biometric performance. These factors directly impact the protected templates derived from iris images when classical Bloom-filters are employed. To this end, we propose and extend our earlier ideas of Morton-filters for obtaining better and reliable templates for iris. Morton filter based TP for iris codes is based on leveraging the intra and inter-class distribution by exploiting low-rank iris codes to derive the stable bits across iris images for a particular subject and also analyzing the discriminable bits across various subjects. Such low-rank non-noisy iris codes enables realizing the template protection in a superior way which not only can be used in constrained setting, but also in relaxed iris imaging. We further extend the work to analyze the applicability to VIS iris images by employing a large scale public iris image database - UBIRIS(v1 & v2), captured in a unconstrained setting. Through a set of experiments, we demonstrate the applicability of proposed approach and vet the strengths and weakness. Yet another contribution of this work stems in assessing the security of the proposed approach where factors of Unlinkability is studied to indicate the antagonistic nature to relaxed iris imaging scenarios.
In this paper we present a framework for secure identification using deep neural networks, and apply it to the task of template protection for face authentication. We use deep convolutional neural networks (CNNs) to learn a mapping from face images t
Augmented reality (AR) or mixed reality (MR) platforms require spatial understanding to detect objects or surfaces, often including their structural (i.e. spatial geometry) and photometric (e.g. color, and texture) attributes, to allow applications t
In this paper, we propose a novel scheme for data hiding in the fingerprint minutiae template, which is the most popular in fingerprint recognition systems. Various strategies are proposed in data embedding in order to maintain the accuracy of finger
The distinctiveness of the human iris has been measured by first extracting a set of features from the iris, an encoding, and then comparing these encoded feature sets to determine how distinct they are from one another. For example, John Daugman mea
Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Moun