Edge-centric multimodal authentication system using encrypted biometric templates

Data security, complete system control, and missed storage and computing opportunities in personal portable devices are some of the major limitations of the centralized cloud environment. Among these limitations, security is a prime concern due to potential unauthorized access to private data. Biometrics, in particular, is considered sensitive data, and its usage is subject to the privacy protection law. To address this issue, a multimodal authentication system using encrypted biometrics for the edge-centric cloud environment is proposed in this study. Personal portable devices are utilized for encrypting biometrics in the proposed system, which optimizes the use of resources and tackles another limitation of the cloud environment. Biometrics is encrypted using a new method. In the proposed system, the edges transmit the encrypted speech and face for processing in the cloud. The cloud then decrypts the biometrics and performs authentication to confirm the identity of an individual. The model for speech authentication is based on two types of features, namely, Mel-frequency cepstral coefficients and perceptual linear prediction coefficients. The model for face authentication is implemented by determining the eigenfaces. The final decision about the identity of a user is based on majority voting. Experimental results show that the new encryption method can reliably hide the identity of an individual and accurately decrypt the biometrics, which is vital for errorless authentication

Model kompanzasyonlu birinci derece istatistikleri ile i-vektörlerin gürbüzlüğünün artırılması

Speaker recognition systems achieved significant improvements over the last decade, especially due to the performance of the i-vectors. Despite the achievements, mismatch between training and test data affects the recognition performance considerably. In this paper, a solution is offered to increase robustness against additive noises by inserting model compensation techniques within the i-vector extraction scheme. For stationary noises, the model compensation techniques produce highly robust systems. Parallel Model Compensation and Vector Taylor Series are considered as state-of-the-art model compensation techniques. Applying these methods to the first order statistics, a noisy total variability space training is aimed, which will reduce the mismatch resulted by additive noises. All other parts of the conventional i-vector scheme remain unchanged, such as total variability matrix training, reducing the i-vector dimensionality, scoring the i-vectors. The proposed method was tested with four different noise types with several signal to noise ratios (SNR) from -6 dB to 18 dB with 6 dB steps. High reductions in equal error rates were achieved with both methods, even at the lowest SNR levels. On average, the proposed approach produced more than 50% relative reduction in equal error rate.Konuşmacı tanıma sistemleri özellikle i-vektörlerin performansı sebebiyle son on yılda önemli gelişmeler elde etmiştir. Bu gelişmelere rağmen eğitim ve test verileri arasındaki uyumsuzluk tanıma performansını önemli ölçüde etkilemektedir. Bu çalışmada, model kompanzasyon yöntemleri i-vektör çıkarımı şemasına eklenerek toplanabilir gürültülere karşı gürbüzlüğü artıracak bir çözüm sunulmaktadır. Durağan gürültüler için model kompanzasyon teknikleri oldukça gürbüz sistemler üretir. Paralel Model Kompanzasyonu ve Vektör Taylor Serileri en gelişmiş model kompanzasyon tekniklerinden kabul edilmektedir. Bu metotlar birinci dereceden istatistiklere uygulanarak toplanabilir gürültülerden kaynaklanan uyumsuzluğu azaltacak gürültülü tüm değişkenlik uzayı eğitimi amaçlanmıştır. Tüm değişkenlik matrisin eğitimi, i-vektör boyutunun azaltılması, i-vektörlerin puanlanması gibi geleneksel i-vektör şemasının diğer tüm parçaları değişmeden kalmaktadır. Önerilen yöntem, 6 dB’lik adımlarla -6 dB’den 18 dB’ye kadar çeşitli sinyal-gürültü oranlarına (SNR) sahip dört farklı gürültü tipi ile test edilmiştir. Her iki yöntemle de en düşük SNR seviyelerinde bile eşit hata oranlarında yüksek azalmalar elde edilmiştir. Önerilen yaklaşım eşik hata oranında ortalama olarak %50’den fazla göreceli azalma sağlamıştır

Scoring heterogeneous speaker vectors using nonlinear transformations and tied PLDa models

Most current state-of-the-art text-independent speaker recognition systems are based on i-vectors, and on probabilistic linear discriminant analysis (PLDA). PLDA assumes that the i-vectors of a trial are homogeneous, i.e., that they have been extracted by the same system. In other words, the enrollment and test i-vectors belong to the same class. However, it is sometimes important to score trials including “heterogeneous” i-vectors, for instance, enrollment i-vectors extracted by an old system, and test i-vectors extracted by a newer, more accurate, system. In this paper, we introduce a PLDA model that is able to score heterogeneous i-vectors independent of their extraction approach, dimensions, and any other characteristics that make a set of i-vectors of the same speaker belong to different classes. The new model, which will be referred to as nonlinear tied-PLDA (NL-Tied-PLDA), is obtained by a generalization of our recently proposed nonlinear PLDA approach, which jointly estimates the PLDA parameters and the parameters of a nonlinear transformation of the i-vectors. The generalization consists of estimating a class-dependent nonlinear transformation of the i-vectors, with the constraint that the transformed i-vectors of the same speaker share the same speaker factor. The resulting model is flexible and accurate, as assessed by the results of a set of experiments performed on the extended core NIST SRE 2012 evaluation. In particular, NL-Tied-PLDA provides better results on heterogeneous trials with respect to the corresponding homogeneous trials scored by the old system, and, in some configurations, it also reaches the accuracy of the new system. Similar results were obtained on the female-extended core NIST SRE 2010 telephone condition

Mixture of PLDA for noise robust i-vector speaker verification

2015-2016 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptRGCPublishe