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It is generally believed that original face image is hard to be reconstructed from its binary face template and therefore, binary face template is secure. In this paper, we find that binary template is not secure and can be attacked by a masquerade attack. A masquerade face image, which may not ”look” similar with the original image, is constructed from binary templates and then employed to access...
In semi-supervised classification boosting, a similarity measure is demanded in order to measure the distance between samples (both labeled and unlabeled). However, most of the existing methods employed a simple metric, such as Euclidian distance, which may not be able to truly reflect the actual similarity/distance. This paper presents a novel similarity learning method based on the geodesic distance...
Biometric cryptosystem (BC) is a very secure approach for template protection because the stored template is encrypted. The key issues in BC approach include(i) limited capability in handling intra-class variations and (ii) binary input is required. To overcome these problems, this paper adopts the concept of discriminative analysis and develops a new binary discriminant analysis (BDA) method to convert...
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