Abstract
In this paper we propose a new range-based face recognition for significant improvement in the recognition rate using an optimized stereo acquisition system. The optimized 3D acquisition system consists of an eyes detection algorithm, facial pose direction distinction, and principal component analysis (PCA). The proposed method is carried out in the YCbCr color space in order to detect the face candidate area. To detect the correct face, it acquires the correct distance of the face candidate area and depth information of eyes and mouth. After scaling, the system transfers the pose change according to the distance. The face is finally recognized by the optimized PCA for each area with the facial pose elements detected. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.
This research was supported by Korean Ministry of Science and Technology under the National Research Laboratory Project, Korean Ministry of Information and Communication under HNRC-ITRC program at Chung-Ang university supervised by IITA, and the Research Grant of Kwangwoon University in 2005.
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Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Analysis, Machine Intelligence 24(1), 34–58 (2002)
Lee, H.C., Park, C.H., Namkung, Y., Namkung, J.C.: An object tracking method using the stereo images. J. IEEK 39 SP-5, 46–58 (2002)
Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic feature subset selection for gender classification: A Comparison Study. In: Sixth IEEE Workshop on Proceedings Applications of Computer Vision (WACV 2002), December 2002, pp. 165–170 (2002)
Rowley, H., Baluja, S., Kanade, T.: Neural Network-based face detection. IEEE Trans. Pattern Analysis, Machine Intelligence 20(1), 203–208 (1998)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proc. of the IEEE, Computer Vision, Pattern Recognition, June 1997, pp. 130–136 (1997)
Samaria, F., Young, S.: HMM based architecture for face identification. Image and Vision Computing 12(8), 537–543 (1994)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: Recognition using class specification linear projection. IEEE Trans. Pattern Analysis, Machine Intelli-gence 19(7), 711–720 (1997)
McLindin, B.A.: Baselining illumination variables for improved facial recognition system performance. In: Video/Image Processing and Multimedia Communications 2003. 4th EURASIP Conference, July 2003, vol. 1, pp. 417–422 (2003)
Chen, L.H., Lin, W.C.: Visual surface segmentation from stereo. Image and Vision Computing 15(2), 95–106 (1997)
Chai, D., Ngan, K.N.: Face Segmentation Using Skin-Color map in Videophone Applications. IEEE Trans. Circuits, Systems for Video Technology 9(4), 551–564 (1999)
Faugeras, O.D.: Three-Dimensional Computer Vision, 4th edn. MIT Press, Cambridge (2001)
IJsselsteijn, W.A., de Ridder, H., Vliegen, J.: Subjective evaluation of stereoscopic images: effects of camera parameters and display duration. IEEE Trans. Circuits, Systems for Video Technology 10(2), 225–233 (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Park, C., Park, S., Shin, J., Paik, J., Namkung, J. (2005). Face Recognition Using Optimized 3D Information from Stereo Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_127
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DOI: https://doi.org/10.1007/11559573_127
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
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