By J. Kittler, R. Ghaderi, T. Windeatt, J. Matas (auth.), Josef Bigun, Fabrizio Smeraldi (eds.)
This publication constitutes the refereed lawsuits of the 3rd overseas convention on Audio- and Video-Based Biometric individual Authentication, AVBPA 2001, held in Halmstad, Sweden in June 2001.
The fifty one revised papers provided including 3 invited papers have been conscientiously reviewed and chosen for inclusion within the publication. The papers are geared up in topical sections on face as biometrics; face photo processing; speech as biometrics and speech processing; fingerprints as biometrics; gait as biometrics; and hand, signature, and iris as biometrics.
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Extra info for Audio- and Video-Based Biometric Person Authentication: Third International Conference, AVBPA 2001 Halmstad, Sweden, June 6–8, 2001 Proceedings
Huang, and P. Rauss, “The FERET database and evaluation procedure for face-recognition algorithms,” Image and Vision Computing, vol. 16, pp. 295– 306, 1998. 18. A. Rosenfeld and H. Wechsler, “Pattern recognition: Historical perspective and future directions,” Int. J. Imaging Syst. , vol. 11, pp. 101–116, 2000. 19. D. L. Swets and J. Weng, “Using discriminant eigenfeatures for image retrieval,” IEEE Trans. on PAMI, vol. 18, no. 8, pp. 831–836, 1996. 20. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol.
Eigenspace dimension. 5 Conclusion We present a face recognition algorithm using corresponding range and gray-level facial images. Experiment results have demonstrated the promising performance of our algorithm. For a larger face database, due to the discriminating representation by integrating the shape and texture information and the robustness of the chosen feature in our system, our algorithm will not deteriorate, which will be evaluated in our future work. Furthermore, other classiﬁers such as Support Vector Machine will be adopted in our system.
The augmented Gabor feature vector introduced in Sect. 2 resides in a space of very high dimensionality, and we first apply PCA for dimensionality reduction: Y (ρ) = P t X (ρ) (4) where P = [P1 P2 · · · Pn ] consists of the n eigenvectors corresponding to the leading eigenvalues of the covariance matrix of X (ρ), n < N and P ∈ RN ×n. The lower dimensional vector Y (ρ) ∈ Rn captures the most expressive features of the original data X (ρ). The PCA output is then processed by the ICA method and its reduced dimension n is determined based upon the ICA sensitivity analysis.