Ajita Rattani

Face Identification by SIFT-based Complete Graph Topology

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.

Template Update Methods in Adaptive Biometric Systems: A Critical Review

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.

Exploiting the “doddington zoo” effect in biometric fusion

Recent research in biometrics has suggested the existence of the “Biometric Menagerie” in which weak users contribute disproportionately to the error rate (FAR and FRR) of a biometric system. The aim of this work is to utilize this observation to design a multibiometric system where information is consolidated on a user-specific basis. To facilitate this, the users in a database are characterized into multiple categories and only users belonging to weak categories are required to provide additional biometric information. The contribution of this work lies in (a) the design of a selective fusion scheme where fusion is invoked only for a subset of users, and (b) evaluating the performance of such a scheme on two public datasets. Experiments on the multi-unit CASIA V3 iris database and multi-unit WVU fingerprint database indicate that selective fusion, as defined in this work, improves overall matching accuracy while potentially reducing overall computational time. This has positive implications in a large-scale system where the throughput can be substantially increased without compromising the verification accuracy of the system.

Critical Analysis of Adaptive Biometric Systems

Biometric-based person recognition poses a challenging problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Solutions in the form of adaptive biometrics have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due in part to the limited understanding and limitations associated with existing adaptation schemes. In view of that the topic of adaptive biometrics has not been systematically investigated, this study works towards filling this gap by surveying the topic from a growing body of the recent literature and by providing a coherent view (critical analysis) of the limitations of the existing systems. In addition, the authors have also identified novel research directions and proposed a novel framework. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.

Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials

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 paper introduces: 1) the use of the Weibull-calibrated SVM (W-SVM), which is relatively robust for open set recognition, as a novel-material detector and a spoof detector and 2) 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: 1) a 97% increase in the error rate of the existing spoof detectors when tested using new spoof materials and 2) up to 44% improvement in spoof detection performance across spoof materials when the proposed adaptive approach is used.

Group-specific score normalization for biometric systems

The problem of biometric menagerie, first pointed out by Doddington et al. (1998), is one that plagues all biometric systems. They observe that only a handful of clients (enrolled users in the gallery) actually contribute disproportionately to recognition errors. While prior literature attempting to reduce this effect focuses on either client-specific score normalization or client-specific decision strategies, in this study, we explore a novel category of approaches: group-specific score normalization. While client-specific score normalization can be negatively impacted by the paucity of genuine score samples, group-specific score normalization is less affected since the matching score samples of different clients belonging to the same group are aggregated. Experimental evidence based on face, fingerprint and iris modalities show that our proposal generally outperforms client-specific score normalization as well as the baseline systems (without any normalization) across all possible operating points (so obtained by changing the decision threshold).

Biometric template update using the graph mincut algorithm : A case study in face verification

A biometric system provides poor performances when the input data exhibit intra-class variations which are not well represented by the enrolled template set. This problem has been recently faced by template update techniques. The majority of the proposed techniques can be regarded as ldquoself-updaterdquo methods, as the system updates its own templates using the recognition results provided by the same templates. However, this approach can only exploit the input data ldquonearrdquo to the current templates resulting in ldquolocalrdquo template optimization, namely, only input samples very similar to the current templates are exploited for update. To address this issue, this paper proposes a ldquoglobalrdquo optimization of templates based on the graph mincut algorithm. The proposed approach can update templates by analysing the underlying structure of input data collected during the systempsilas operation. This is achieved by a graph drawn using a pair-wise similarity measure between enrolled and input data. Investigation of this novel template update technique has been done by its application to face verification, as a case study. The reported results show the effectiveness of the proposed technique in comparison to state of art self-update techniques.

Automatic adaptation of fingerprint liveness detector to new spoof materials

A fingerprint liveness 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 liveness detectors are learning-based and rely on a set of training images. Consequently, the performance of a liveness detector significantly degrades upon encountering spoofs fabricated using new materials not used during the training stage. To mitigate the security risk posed by new spoofs, it is necessary to automatically adapt the liveness detector to new spoofing materials. The aim of this work is to design a scheme for automatic adaptation of a liveness detector to novel spoof materials encountered during the operational phase. To facilitate this, a novel-material detector is used to flag input images that are deemed to be made of a new spoofing material. Such flagged images are then used to retrain the liveness detector. Experiments conducted on the LivDet 2011 database suggest (i) a 62% increase in the error rate of existing liveness detectors when tested using new spoof materials, and (ii) upto 46% improvement in liveness detection performance across spoof materials when the proposed adaptive approach is used.

Capturing large intra-class variations of biometric data by template co-updating

The representativeness of a biometric template gallery to the novel data has been recently faced by proposing ldquotemplate updaterdquo algorithms that update the enrolled templates in order to capture, and represent better, the subjectpsilas intra-class variations. Majority of the proposed approaches have adopted ldquoselfrdquo update technique, in which the system updates itself using its own knowledge. Recently an approach named template co-update, using two complementary biometrics to ldquoco-updaterdquo each other, has been introduced. In this paper, we investigate if template co-update is able to capture intra-class variations better than those captured by state of art self update algorithms. Accordingly, experiments are conducted under two conditions, i.e., a controlled and an uncontrolled environment. Reported results show that co-update can outperform ldquoselfrdquo update technique, when initial enrolled templates are poor representative of the novel data (uncontrolled environment), whilst almost similar performances are obtained when initial enrolled templates well represent the input data (controlled environment).

Analysis of user-specific score characteristics for spoof biometric attacks

Several studies in biometrics have confirmed the existence of user-specific score characteristics for genuine and zero-effort impostor score distributions. As an important consequence, biometric users contribute disproportionately to the FRR (false reject rate) and FAR (false accept rate) of the system. This phenomena is also know as the Doddington zoo effect. Recent studies indicate the vulnerability of unimodal and multibiometric systems to spoof attacks. The aim of this study is to analyze the score characteristics for spoof attacks. Such an analysis will 1) help improve our understanding of the Doddington zoo effect under spoof attacks; and 2) allow us to design biometric classifiers that are more robust to such attacks. The contributions of this paper are as follows: a) examining the existence of user-specific score characteristics for spoof attacks and b) analyzing the correlation between user-specific score characteristics obtained on genuine (as well as zero-effort impostor) and non zero-effort impostor (spoof) score distributions. Experiments conducted on the LivDet09 spoofed fingerprint database confirms that biometric user-groups exhibit different degrees of vulnerability to spoof attacks as well. Further, moderate negative correlation may exist between users who are difficult to recognize and their vulnerability to spoof attacks.