Download Audio- and Video-Based Biometric Person Authentication: by J. Kittler, R. Ghaderi, T. Windeatt, J. Matas (auth.), Josef PDF

By J. Kittler, R. Ghaderi, T. Windeatt, J. Matas (auth.), Josef Bigun, Fabrizio Smeraldi (eds.)

This e-book constitutes the refereed court cases of the 3rd foreign 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 rigorously reviewed and chosen for inclusion within the ebook. The papers are equipped in topical sections on face as biometrics; face photograph 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

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15. B. Moghaddam and A. Pentland, “Probabilistic visual learning for object representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696–710, 1997. 16. A. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, vol. 381, no. 13, pp. 607–609, 1996. 17. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, “The FERET database and evaluation procedure for face-recognition algorithms,” Image and Vision Computing, vol.

2. FRR of the Previous and the Proposed Methods with Yale Database. 1 3 5 7 9 Person # 11 13 15 Fig. 3. FAR of the Previous and the Proposed Methods with Yale Database. 5 Concluding Remarks This paper proposed a method of face recognition by integrating GA into SVM. Compared to the previous methods which use the whole image as an input vector, this method uses the feature set extracted by GA as an input vector. Our proposed method has several advantages. By selecting the feature set which has superior performance in recognizing faces, the use of unnecessary information of the faces can be avoided and Face Recognition Using Support Vector Machines 37 the memory requirement can be decreased significantly.

Turk, M. : Eigenface for Recognition. Journal of Cognitive Neuroscience 3 (1991) 72–86. 2. : Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 7 (1997) 775–779. 3. : 3D Human Face Recognition Using Point Signature. Fourth IEEE International Conference on Automatic Face and Gesture Recognition (2000) 233–238. 4. S. : Point Signature: A New Representation for 3D Object Recognition. International Journal on Computer Vision 1 (1997) 63-85. 5.

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