Multi-modal person recognition for vehicular applications

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.