Browse > Article

Sliding Active Camera-based Face Pose Compensation for Enhanced Face Recognition  

장승호 (중앙대학교)
김영욱 (전자부품연구원)
박창우 (전자부품연구원)
박장한 (광운대학교)
남궁재찬 (광운대학교)
백준기 (중앙대학교)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.41, no.6, 2004 , pp. 155-164 More about this Journal
Abstract
Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.
Keywords
sliding active cameras; convex-hull; fuzzy inference; principal component analysis; face recognition;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 F. Samaria and S. Young, 'HMM based architecture for face identification,' Image and Vision Computing, vol. 12, pp. 537-543, 1994   DOI   ScienceOn
2 P. Penev and J. Attick, 'Local Feature Analysis: a general statistical theory for object representation,' Network: Computation in Neural Systems, vol. 7, no. 3, pp. 447-500, 1996
3 P. Comon, 'Independent component analysis, A new concept?,' Signal Processing, vol. 36, no. 3, pp.287-314, 1994   DOI   ScienceOn
4 C. W. Park, Y. O. Kim, H. K. Sung, 'Multiple Face Segmentation and Tracking based on Robust Hausdorff Distance Matching,' Int. Journal of Fuzzy Logic and Int. System, vol. 3, no. 1, pp. 87-92, 2003   DOI
5 L. M. Brown, Y. L. Tian, 'Comparative study of coarse head pose estimation,' IEEE Workshop on Motion and Video Computing, pp. 125-130, 2002   DOI
6 Y. O. Kim, J. k. Paik, J. G. Heo, A. Koschan, B. Abidi and M. Abidi, 'Automatic Face Region Tracking for Highly Accurate Face Recognition in Unconstrained Environments,' IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 29-36, 2003   DOI
7 C. W. Park, E. Kim and M. Park, 'Human Face Detection via Characterized Convex Regional Relationship in Color Images,' IEICE Trans. INF. & SYST, vol. E86-D, no. 4, pp. 759-762, 2003
8 T. G. Lee, S. K. Park and M. S. Kim, 'Face Detection and Recognition with Multiple Appearance Models for Mobile Robot Application,' Proceedings ICCAS, pp. 215-218, 2002
9 A. J. O'Toole, H. Abdi, K. A. Deffenbacher and D. Valentin, 'Low dimensional representation of faces in high dimensions of the face space,' J. Opt. Soc. Am. A, vol. 10, pp. 405-411, 1993   DOI
10 J. Zang, Y. Yan, and M. Lades, 'Face recognition: Eigenface, elastic matching, and neural nets,' Proc. IEEE, vol. 85, no. 9, pp. 1423-1435, 1997   DOI   ScienceOn
11 O. Nakamura, S. mathur and T. Minami, 'Identification of human faces based on isodensity maps,' Pattern Recognition, vol. 24, pp. 236-272, 1991   DOI   ScienceOn
12 C. H. The and R. T. Chin, 'On image analysis by the method of moments,' IEEE Trans. Patt. Anal. Machine Intell. vol. 10, pp. 496-513, 1988   DOI   ScienceOn
13 S. R. Dubois and F. H. Grantz, 'A autoregressive model approach to two-demensional shape classification,' IEEE Trans. Patt. Anal. Machine Intell. vol. 8, pp. 56-65, 1986
14 M. Turk and A. Pentland, 'Eigenfaces for recognition,' Journal, Cognitive Neuroscience, vol. 3, pp 72-86, 1991
15 S. Gong, S. J. McKenna, A. Psarrou, 'Dynamic Vision,' Imperical College press, 2000
16 L. Sirovich and M. Kirby, 'Low dimensional procesure for the characterization of human faces,' J. Opt. Soc. Am. vol. 4, pp. 519-524, 1987   DOI
17 Z, Q. Hong, 'Algebraic feature extraction of images for recognition,' Pattern Recognition, vol. 24, pp. 211-219, 1991   DOI   ScienceOn
18 L. Sirivich and M.kirby, 'Low-dimensional procedure for the characterization of human faces,' J. Opt. Soc. Amer., vol. 4, pp. 519-524, 1987   DOI
19 E. Oja, 'Principal components, minor components, and linear neural networks,' Neural Networks, vol. 5, pp. 927-935, 1992   DOI   ScienceOn
20 H. Rowley, S. Baluja and T. Kanade, 'Neural Network-Based Face Detection,' IEEE Trans. Patt. Anal. Machine Intell., vol. 20, no. 1, pp. 203-208, 1998   DOI   ScienceOn
21 E. Osuna, R. Freund and F. Girosi, 'Training support vector machines: an application to face detection,' Proc. IEEE Conf. Computer Vision, Pattern Recognition, pp. 130-136, 1997   DOI
22 P. Belhumeur, J. Hespanha, and D. Kiegman, 'Eigenfaces vs. Fisherfaces: Recognition Using Class Specfic Linear Projection,' IEEE Trans. PAMI, vol. 19, no. 7, pp. 711-720, 1997   DOI   ScienceOn
23 L. Wiskott, J. Fellous, N. Krger and C. Malsburg, 'Face Recognition by Elastic Bunch Graph Matching,' IEEE Trans. PAMI, vol. 19, pp. 775-779, 1997   DOI   ScienceOn