Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Journal of Cognitive Neuroscience
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Multimodal Person Recognition for Human-Vehicle Interaction
IEEE MultiMedia
Classifier ensembles: Select real-world applications
Information Fusion
Simulation Driven Experiment Control in Driver Assistance Assessment
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
Driver Recognition Using Gaussian Mixture Models and Decision Fusion Techniques
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
User identity verification via mouse dynamics
Information Sciences: an International Journal
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In this paper, we present biometric person recognition experiments in a real-world car environment using speech, face, and driving signals. We have performed experiments on a subset of the in-car corpus collected at the Nagoya University, Japan. We have used Mel-frequency cepstral coefficients (MFCC) for speaker recognition. For face recognition, we have reduced the feature dimension of each face image through principal component analysis (PCA). As for modeling the driving behavior, we have employed features based on the pressure readings of acceleration and brake pedals and their time-derivatives. For each modality, we use a Gaussian mixture model (GMM) to model each person's biometric data for classification. GMM is the most appropriate tool for audio and driving signals. For face, even though a nearest-neighbor-classifier is the preferred choice, we have experimented with a single mixture GMM as well. We use background models for each modality and also normalize each modality score using an appropriate sigmoid function. At the end, all modality scores are combined using a weighted sum rule. The weights are optimized using held-out data. Depending on the ultimate application, we consider three different recognition scenarios: verification, closed-set identification, and open-set identification. We show that each modality has a positive effect on improving the recognition performance.