Abstract. Template representativeness is a fundamental problem in a biometric recognition system. The performance of the system degrades if the enrolled templates are un-representative of the substantial intra-class variations encountered in the input biometric samples. Recently, several template updates methods based on supervised and semi-supervised learning have been proposed in the literature with an aim to update the enrolled templates to the intra-class variations of the input data. However, the state of art related to template update is still in its infancy. This paper presents a critical review of the current approaches to template updating in order to analyze the state of the art in terms of advancement reached and open issues remain.

Abstract-This paper presents a new face identification system based on Graph Matching Technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn on SIFT features which are invariant to rotation, scaling and translation. Face projections on images, represented by a graph, can be matched onto new images by maximizing a similarity function taking into account spatial distortions and the similarities of the local features. Two graph based matching techniques have been investigated to deal with false pair assignment and reducing the number of features to find the optimal feature set between database and query face SIFT features. The experimental results, performed on the BANCA database, demonstrate the effectiveness of the proposed system for automatic face identification.

Abstract-The aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics. The proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities, and making the two pointsets compatible for concatenation. Moreover, to handle the 'problem of curse of dimensionality', the feature pointsets are properly reduced in dimension. Different feature reduction techniques are implemented, prior and after the feature pointsets fusion, and the results are duly recorded. The fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching, or the Delaunay triangulation. Comparative experiments are conducted on chimeric and real databases, to assess the actual advantage of the fusion performed at the feature extraction level, in comparison to the matching score level.

Abstract

A fingerprint spoof detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. Most spoof detectors are learning-based and rely on a set of training images. Consequently, the performance of any such spoof detector significantly degrades when encountering spoofs fabricated using novel materials not found in the training set. In real-world applications, the problem of fingerprint spoof detection must be treated as an open set recognition problem where incomplete knowledge of the fabrication materials used to generate spoofs is present at training time, and novel materials may be encountered during system deployment. To mitigate the security risk posed by novel spoofs, this work introduces: (a) the use of the Weibullcalibrated SVM (W-SVM), which is relatively robust for open set recognition, as a novel-material detector and a spoof detector, and (b) a scheme for the automatic adaptation of the W-SVM-based spoof detector to new spoof materials that leverages interoperability across classifiers. Experiments conducted on new partitions of the LivDet 2011 database designed for open set evaluation suggest (i) a 97% increase in the error rate of existing spoof detectors when tested using new spoof materials, and (ii) up to 44% improvement in spoof detection performance across spoof materials when the proposed adaptive approach is used.