Abstract
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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Erzin, E., Yemez, Y., Tekalp, A.M., Erçil, A., Erdogan, H., Abut, H.: Multimodal Person Identification for Human Vehicle Interaction. accepted for publication in the IEEE Signal Processing Magazine Special Issue on Man-Machine Communication, (September 2005) (to appear)
Kawaguchi, N., Matsubara, S., Kishida, I., Irie, Y., Murao, H., Yamaguchi, Y., Takeda, K., Itakura, F.: Construction and Analysis of the Multi-layered In-car Spoken Dialogue Corpus. In: DSP in Vehicular and Mobile Systems, Ch. 1. Springer, New York (2005)
Mermelstein, P., Davis, S.B.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoustics, Speech and Signal Processing 28, 357–366 (1980)
Ekenel, H.K., Bilgin, S.Y., Eden, I., Kirisçi, M., Erdogan, H., Erçil, A.: Multimodal Person Verification from Video Sequences. In: Proceedings, SWIM 2004, Maui, HI (January 2004)
Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models. Speech Communications 17, 91–108 (1995)
Dempster, A., Laird, N., Rubin, M.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Royal Statistical Soc. 39(1) (1978)
Zhao, W., Chellappa, R., Phillips, J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 586–591 (1991)
Zhang, Y.Y.J., Lades, M.: Face recognition: Eigenface, elastic matching, and neural nets. Proceedings of the IEEE, 85(9), 1423–1435 (1997)
Zhao, W.: Subspace Methods in Object/Face Recognition. In: Proc. Int. Joint Conf. on Neural Networks (1999)
Igarashi, K., Miyajima, C., Itou, K., Takeda, K., Abut, H., Itakura, F.: Biometric Identification Using Driving Behavior. In: Proceedings IEEE ICME 2004, Taipei, Taiwan, June 27-30 (2004)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans. On Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics (August 2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Erdoğan, H. et al. (2005). Multi-modal Person Recognition for Vehicular Applications. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_37
Download citation
DOI: https://doi.org/10.1007/11494683_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
eBook Packages: Computer ScienceComputer Science (R0)