Robust multi-modal and multi-unit feature level fusion of face and iris biometrics

Multi-biometrics has recently emerged as a mean of more robust and effcient personal verification and identification. Exploiting information from multiple sources at various levels i.e., feature, score, rank or decision, the false acceptance and rejection rates can be considerably reduced. Among all, feature level fusion is relatively an understudied problem. This paper addresses the feature level fusion for multi-modal and multi-unit sources of information. For multi-modal fusion the face and iris biometric traits are considered, while the multi-unit fusion is applied to merge the data from the left and right iris images. The proposed approach computes the SIFT features from both biometric sources, either multi- modal or multi-unit. For each source, the extracted SIFT features are selected via spatial sampling. Then these selected features are finally concatenated together into a single feature super-vector using serial fusion. This concatenated feature vector is used to perform classification. Experimental results from face and iris standard biometric databases are presented. The reported results clearly show the performance improvements in classification obtained by applying feature level fusion for both multi-modal and multi-unit biometrics in comparison to uni-modal classification and score level fusion

Threshold-optimized decision-level fusion and its application to biometrics

Fusion is a popular practice to increase the reliability of biometric verification. In this paper, we propose an optimal fusion scheme at decision level by the AND or OR rule, based on optimizing matching score thresholds. The proposed fusion scheme will always give an improvement in the Neyman–Pearson sense over the component classifiers that are fused. The theory of the threshold-optimized decision-level fusion is presented, and the applications are discussed. Fusion experiments are done on the FRGC database which contains 2D texture data and 3D shape data. The proposed decision fusion improves the system performance, in a way comparable to or better than the conventional score-level fusion. It is noteworthy that in practice, the threshold-optimized decision-level fusion by the OR rule is especially useful in presence of outliers

Multimodal biometrics based on identification and verification system

The need for an increase of reliability and security in a biometric system is motivated by the fact that there is no single technology that can realize multi-purpose scenarios. Experimental results showed that the recognition rate of Heart Sound Identification (HSI) model is 81.9%, while the rate for Speaker Identification (SI) model is 99.3% from 20 clients and 70 impostors. Heart Sound-Verification (HSV) provides an average Equal Error Rate (EER) of 13.8%, while the average EER for the Speaker Verification model (SV) is 2.1%. Electrocardiogram Identification (ECGI), on the other hand, provides an accuracy of 98.5% and ECG Verification (ECGV) EER of 4.5%. In order to reach a higher security level, an alternative multimodal and a fusion technique were implemented into the system. Through the performance analysis of the three biometric system and their combination using two multimodal biometric score level fusion, this paper found the optimal combination of those systems. The best performance of the work is based on simple-sum score fusion, with a piecewise-linear normalization technique which provides an EER of 0.7%

Design and implementation of a multi-modal biometric system for company access control

This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman\u2019s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user\u2019s face only or the serial acquisition of both the user\u2019s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \u201cresponse behaviors\u201d according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Feature Level Fusion of Face and Fingerprint Biometrics

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.Comment: 6 pages, 7 figures, conferenc