Recognition of Biometric Unlock Pattern by GMM-UBM

International audienceUnlock patterns are used for authentication in mobile smart devices, yet they are vulnerable to attacks, since only the pattern draw is required. Extra biometric data of the user while drawing the unlock pattern passwords may strengthen the authentication, such as the speed of drawing, the pressure of the finger on the touch screen. Such biometric modality is referred to as behavioral biometrics. Besides, voice is also a behavioral biometric modality, as well as a physiological one. Hence, statistical models such as Gaussian mixture models (GMM) with universal background modeling (UBM) are widely used in speaker verification systems. In this work, we propose to apply and adapt a framework usually dedicated to speaker verification to recognize the unlock patterns based on users' behavior. We evaluate the performance using equal error rate for different combinations of features and varying number of mixtures. As a result of the combination of features, an equal error rate as low as 9.25% on average is obtained, which is promising for a preliminary study on GMM-UBM applied to unlock pattern based biometric recognition

Human abnormal behavior impact on speaker verification systems

Human behavior plays a major role in improving human-machine communication. The performance must be affected by abnormal behavior as systems are trained using normal utterances. The abnormal behavior is often associated with a change in the human emotional state. Different emotional states cause physiological changes in the human body that affect the vocal tract. Fear, anger, or even happiness we recognize as a deviation from a normal behavior. The whole spectrum of human-machine application is susceptible to behavioral changes. Abnormal behavior is a major factor, especially for security applications such as verification systems. Face, fingerprint, iris, or speaker verification is a group of the most common approaches to biometric authentication today. This paper discusses human normal and abnormal behavior and its impact on the accuracy and effectiveness of automatic speaker verification (ASV). The support vector machines classifier inputs are Mel-frequency cepstral coefficients and their dynamic changes. For this purpose, the Berlin Database of Emotional Speech was used. Research has shown that abnormal behavior has a major impact on the accuracy of verification, where the equal error rate increase to 37 %. This paper also describes a new design and application of the ASV system that is much more immune to the rejection of a target user with abnormal behavior.Web of Science6401274012

Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System

In this paper, we explore the encoding/pooling layer and loss function in the end-to-end speaker and language recognition system. First, a unified and interpretable end-to-end system for both speaker and language recognition is developed. It accepts variable-length input and produces an utterance level result. In the end-to-end system, the encoding layer plays a role in aggregating the variable-length input sequence into an utterance level representation. Besides the basic temporal average pooling, we introduce a self-attentive pooling layer and a learnable dictionary encoding layer to get the utterance level representation. In terms of loss function for open-set speaker verification, to get more discriminative speaker embedding, center loss and angular softmax loss is introduced in the end-to-end system. Experimental results on Voxceleb and NIST LRE 07 datasets show that the performance of end-to-end learning system could be significantly improved by the proposed encoding layer and loss function.Comment: Accepted for Speaker Odyssey 201