Detecting replay attacks in audiovisual identity verification

We describe an algorithm that detects a lack of correspondence between speech and lip motion by detecting and monitoring the degree of synchrony between live audio and visual signals. It is simple, effective, and computationally inexpensive; providing a useful degree of robustness against basic replay attacks and against speech or image forgeries. The method is based on a cross-correlation analysis between two streams of features, one from the audio signal and the other from the image sequence. We argue that such an algorithm forms an effective first barrier against several kinds of replay attack that would defeat existing verification systems based on standard multimodal fusion techniques. In order to provide an evaluation mechanism for the new technique we have augmented the protocols that accompany the BANCA multimedia corpus by defining new scenarios. We obtain 0% equal-error rate (EER) on the simplest scenario and 35% on a more challenging one

Multimodal Fusion of Polynomial Classifiers for Automatic Person Recognition

With the prevalence of the information age, privacy and personalization are forefront in today\u27s society. As such, biometrics are viewed as essential components of current and evolving technological systems. Consumers demand unobtrusive and noninvasive approaches. In our previous work, we have demonstrated a speaker verification system that meets these criteria. However, there are additional constraints for fielded systems. The required recognition transactions are often performed in adverse environments and across diverse populations, necessitating robust solutions. There are two significant problem areas in current generation speaker verification systems. The first is the difficulty in acquiring clean audio signals (in all environments) without encumbering the user with a head-mounted close-talking microphone. Second, unimodal biometric systems do not work with a significant percentage of the population. To combat these issues, multimodal techniques are being investigated to improve system robustness to environmental conditions, as well as improve overall accuracy across the population. We propose a multimodal approach that builds on our current state-of-the-art speaker verification technology. In order to maintain the transparent nature of the speech interface, we focus on optical sensing technology to provide the additional modality–giving us an audio-visual person recognition system. For the audio domain, we use our existing speaker verification system. For the visual domain, we focus on lip motion. This is chosen, rather than static face or iris recognition, because it provides dynamic information about the individual. In addition, the lip dynamics can aid speech recognition to provide liveness testing. The visual processing method makes use of both color and edge information, combined within a Markov random field (MRF) framework, to localize the lips. Geometric features are extracted and input to a polynomial classifier for the person recognition process. A late integration approach, based on a probabilistic model, is employed to combine the two modalities. The system is tested on the XM2VTS database combined with AWGN (in the audio domain) over a range of signal-to-noise ratios

One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information

Lip-based biometric authentication (LBBA) is an authentication method based on a person's lip movements during speech in the form of video data captured by a camera sensor. LBBA can utilize both physical and behavioral characteristics of lip movements without requiring any additional sensory equipment apart from an RGB camera. State-of-the-art (SOTA) approaches use one-shot learning to train deep siamese neural networks which produce an embedding vector out of these features. Embeddings are further used to compute the similarity between an enrolled user and a user being authenticated. A flaw of these approaches is that they model behavioral features as style-of-speech without relation to what is being said. This makes the system vulnerable to video replay attacks of the client speaking any phrase. To solve this problem we propose a one-shot approach which models behavioral features to discriminate against what is being said in addition to style-of-speech. We achieve this by customizing the GRID dataset to obtain required triplets and training a siamese neural network based on 3D convolutions and recurrent neural network layers. A custom triplet loss for batch-wise hard-negative mining is proposed. Obtained results using an open-set protocol are 3.2% FAR and 3.8% FRR on the test set of the customized GRID dataset. Additional analysis of the results was done to quantify the influence and discriminatory power of behavioral and physical features for LBBA.Comment: 28 pages, 10 figures, 7 table

Survey Analysis on Secured user Authentication through Biometric Recognition

Secured user authentication is the process of verifying the user authenticity. Biometric authentication is the human identification system employed to match the biometric characteristics of user for verifying the authenticity. Biometric identifiers are exclusive, making it harder to hack accounts using them. Common types of biometrics comprise the fingerprint scanning verifies authentication based on a user's fingerprints Face recognition and voice recognition are employed in real-time application for improving the security level in different application scenarios. Face recognition is a method of identifying or verifying the individual identity using their face expression. Voice recognition is the ability of machine to receive and interpret the dictation to understand. Many researchers carried out their research on different face and voice recognition methods. But, recognition accuracy was not improved with minimum time consumption by existing biometric recognition method. In this research, different recognition methods are reviewed using biometric recognition method for user authentication. The recognition methods are efficiently on human faces dataset with respect to performance metrics like recognition accuracy, error rate, and recognition time