Comparative Study And Analysis Of Quality Based Multibiometric Technique Using Fuzzy Inference System

Biometric is a science and technology of measuring and analyzing biological data i.e. physical or behavioral traits which is able to uniquely recognize a person from others. Prior studies of biometric verification systems with fusion of several biometric sources have been proved to be outstanding over single biometric system. However, fusion approach without considering the quality information of the data used will affect the system performance where in some cases the performances of the fusion system may become worse compared to the performances of either one of the single systems. In order to overcome this limitation, this study proposes a quality based fusion scheme by designing a fuzzy inference system (FIS) which is able to determine the optimum weight to combine the parameter for fusion systems in changing conditions. For this purpose, fusion systems which combine two modalities i.e. speech and lip traits are experimented. For speech signal, Mel Frequency Cepstral Coefficient (MFCC) is used as features while region of interest (ROI) of lip image is employed as lip features. Support vector machine (SVM) is then executed as classifier to the verification system. For validation, common fusion schemes i.e. minimum rule, maximum rule, simple sum rule, weighted sum rule are compared to the proposed quality based fusion scheme. From the experimental results at 35dB SNR of speech and 0.8 quality density of lip, the EER percentages for speech, lip, minimum rule, maximum rule, simple sum rule, weighted sum rule systems are observed as 5.9210%, 37.2157%, 33.2676%, 31.1364%, 4.0112% and 14.9023%, respectively compared to the performances of sugeno-type FIS and mamdani-type FIS i.e. 1.9974% and 1.9745%

Multimodal biometrics score level fusion using non-confidence information

Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above

A Novel Approach to Improve Biometric Recognition Using Rank Level Fusion

This paper proposes a novel approach for rank level fusion which gives improved performance gain verified by experimental results. In the absence of ranked features and instead of using the entire template, we propose using K partitions of the template. The approach proposed in the paper is useful for generating sequential ranks and survivor lists on partitions of template to boost confidence levels by incorporating information from partitions. The proposed algorithm iteratively generates ranks for each partition of the user template. Ranks from template partitions are consolidated to estimate the fusion rank for the classification. This paper investigates rank level fusion for palmprint biometric using two approaches: (1) fixed threshold and resulting survivor list, and (2) iterative thresholds and iteratively refined survivor list. The above approaches achieve similar performances as related manifestations of fusion architecture. The experimental results support the proposition of high in-template similarity of palmprint for a user and its relevance to the intra-modal fusion framework. Experimental results using proposed approach on real palmprint data from 100 users show superior performance with recognition accuracy of 99 % as compared to recognition accuracy of 95 % achieved with the conventional approach. I