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

Identity verification using voice and its use in a privacy preserving system

Since security has been a growing concern in recent years, the field of biometrics has gained popularity and became an active research area. Beside new identity authentication and recognition methods, protection against theft of biometric data and potential privacy loss are current directions in biometric systems research. Biometric traits which are used for verification can be grouped into two: physical and behavioral traits. Physical traits such as fingerprints and iris patterns are characteristics that do not undergo major changes over time. On the other hand, behavioral traits such as voice, signature, and gait are more variable; they are therefore more suitable to lower security applications. Behavioral traits such as voice and signature also have the advantage of being able to generate numerous different biometric templates of the same modality (e.g. different pass-phrases or signatures), in order to provide cancelability of the biometric template and to prevent crossmatching of different databases. In this thesis, we present three new biometric verification systems based mainly on voice modality. First, we propose a text-dependent (TD) system where acoustic features are extracted from individual frames of the utterances, after they are aligned via phonetic HMMs. Data from 163 speakers from the TIDIGITS database are employed for this work and the best equal error rate (EER) is reported as 0.49% for 6-digit user passwords. Second, a text-independent (TI) speaker verification method is implemented inspired by the feature extraction method utilized for our text-dependent system. Our proposed TI system depends on creating speaker specific phoneme codebooks. Once phoneme codebooks are created on the enrollment stage using HMM alignment and segmentation to extract discriminative user information, test utterances are verified by calculating the total dissimilarity/distance to the claimed codebook. For benchmarking, a GMM-based TI system is implemented as a baseline. The results of the proposed TD system (0.22% EER for 7-digit passwords) is superior compared to the GMM-based system (0.31% EER for 7-digit sequences) whereas the proposed TI system yields worse results (5.79% EER for 7-digit sequences) using the data of 163 people from the TIDIGITS database . Finally, we introduce a new implementation of the multi-biometric template framework of Yanikoglu and Kholmatov [12], using fingerprint and voice modalities. In this framework, two biometric data are fused at the template level to create a multi-biometric template, in order to increase template security and privacy. The current work aims to also provide cancelability by exploiting the behavioral aspect of the voice modality

Saklı modeli kullanarak ses ile metin bağımlı kimlik doğrulama

Bu çalışmada metin bağımlı bir konuşmacı doğrulama sistemi tanıtılmaktadır. Doğrulanmak istenen bir işitsel parola örneği, iddia edilen kişiye ait parolanın kullanıcılardan bağımsız olarak oluşturulmuş ve fonem modeline dayalı bir Saklı Markov Modeli ile hizalanır. Daha sonra, hizalanan bu parolanın öznitelik vektörü ve kullanıcının verdiği referanslardan elde edilen ortalama parola vektörü arasındaki uzaklık hesaplanarak, sistem kabul veya red kararı verir. Otuz kullanıcıdan alınan toplam 600 adet işitsel parola örneği ile yapılan deneylerde sistemin eşit hata oranı %7’nin altında gözlemlenmiştir. Metin bağımlı bir sistemin kullanılması ve doğrulayıcıda kullanıcıya özel eşik değerlerinin uygulanması sistemin doğrulamadaki başarı oranını arttırıcı faktörler olmuştur