A REVIEW ON ROBUST FACE DETECTION
METHOD USING GABOR WAVELETS
Puneet Kumar Goyal1, Mradul Jain1
Senior Assistant Professor, Dept. of CSE, ABESEC, Ghaziabad, Uttar Pradesh, India1
Associate. Professor, Dept. of CSE, ABESEC, Ghaziabad, Uttar Pradesh, India2 |
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Abstract
This paper describes a face detection method using Artificial Neural Network (ANN) and Gabor filters. This method achieves rotation invariant and extremely high face detection rate using Gabor wavelets. Gabor filters have optimal localization properties in both spatial and frequency domain. By using these desirable characteristics, Gabor filters extract facial features from the local image. These extracted features work as the input to image classifier which is a Feed Forward Neural Network (FFNN).This network works on a reduced feature subspace learned by an approach simpler than principal component analysis (PCA). Face classification is currently implemented in software. This study gives an impression of Gabor filters in image processing and emphasis on its characteristics of spatial locality and orientation selectivity.
- Ming-Husan Yang, David J.Kriegman, Narendra Ahuja, “Detecting Faces in Images: A Survey “, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, pp. 34-58 January 2002.
- H. A. Rowley, S. Baluja, T. Kanade, “Neural Network-Based Face Detection”, IEEE Transactiono n Pattern Analysis and Machine Intelligence, Vol.20, pp. 39-51, 1998.
- Zhang ZhenQiu, Zhu Long, S.Z. Li, Zhang Hong Jiang, “Real-time Multi-View Face Detection”, Proceeding of the Fifth IEEE International Face Conference on Automatic Faceand Gesture Recognition, pp 142-147, 20-21 May 2002.
- Gavrila, D.M; Philomin, V.”Real-Time Object detection for Smart Vehicules”. International Conference on Computer Vision (ICCV99), Vol. 1, 20-25 September 1999.
- Rolf F. Molz, Paulo M. Engel, Fernando G. Moraes, Lionel Torres, Michel Robert,”System Prototyping Dedicated to Neural Network Real-Time Image Processing”, ACM/SIGDA Ninth International Symposium on Field Programmable Gate Arrays( FPGA 2001).
- Haisheng Wu, John Zelek,” A Multi-classifier Based Real-time Face Detection System”, Journal of IEEE Transaction on Robotics and Automation, 2003.
- Theocharis Theocharides, Gregory Link, Vijaykrishnan Narayanan, Mary Jane Irwin, “Embedded Hardware Face Detection”, 17th International Conference on VLSI Design, Mumbai,India, January 5-9, 2004.
- Fan Yang and Michel Paindavoine,”Prefiltering for Pattern Recognition Using Wavelet Transform and Face Recognition”,16th International Conference on Microelectronics, Tunisia 2004.
- Fan Yang and Michel Paindavoine,”Prefiltering for pattern Recognition Using Wavelet Transform and Neural Networks”, Adavances in imaging and Electron Physics, Vol. 127, 2003.
- Xiaoguang Li and shawki Areibi,”A Hardware/Software co-design Approach for Face Recognition”, 16th International Conference on Microelectronics, Tunisia 2004.
- Fan Yang and Michel Paindavoine,”Implementation of an RBF Neural Network on Embedded Systems: Real-Time Face Tracking and Identity Verification”, IEEE Transactions on Neural Networks, vol.14, pp. 1162-1175, September 2003.
- R. McCready, “Real-Time Face Detection on A Configurable Hardware System”, International Symposium on Field Programmable Gate Arrays, Montery, California, United States, 2000
- D. Gajski, N. Dutt, A. Wu, “High-Level Synthesis: Introduction to Chipand System Design”, Kluwer Academic Publishers,Boston, 1992.
- T. Kanade, “Picture Processing by Computer Complex and Recognition of Human Faces”.Technical Report, Kyoto University, Dept. of Information Science, 1973.
- S. Lin, S. Kung, and L. Lin, “Face Recognition/Detection by Probabilistic DecisionBased Neural Network,” IEEE Trans. Neural Networks, Vol.8, pp.114-132, 1997.
- R. Brunelli, T. Poggio, “Face Recognition: Features vs. Templates,” IEEE Trans. on PAMI, Vol. 12, No. 1, Jan. 1990. M. H. Yang, N. Ahuja, and D. Kriegman, “A survey on face “.
- S. Ranganath and K. Arun, “Face Recognition using Ransform Features and Neural Network,” Pattern Recognition, Vol.30,pp.1615-1622, 1997.
- S. Lawrence, C. Giles, A. Tsoi, and A. Back, “Face Recognition: A Convolutional Neural Network Approach,” IEEE Trans. on pp.98-113,1997.
- M. Turk and A. Pentland. “Eigenfaces for Recognition.” Journal of Cognitive Science, pp.71-86, 1991.
- P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using class Specific linear projection,” IEEE Trans. on PAMI, Vol.19, 1997.
- D. Perkins, “A Definition of Caricature and Recognition,”Studies in the Anthropology of Visual Commun.,Vol. 2, pp. 1-24, 1975.
- L. Wiskott, J. M. Fellous, N. Krüger and Christoph von der Malsburg, “Face Recognition by Elastic Graph Matching,”In Intelligent Biometric110 Techniques in fingerprint and Face Recognition, CRC Press, Chapter 11, pp. 355-396, 1999.
- J. G. Daugman, “Complete Discrete 2-D Gabor Transform by Neural networks for Image Analysis and Compression,” IEEE Trans. On Acoustics, Speech and Signal Processing, Vol. 36, No.7, pp.1169-1179, 1988.
- M. J. Lyons, J.Budynek and S. Akamatsu, “Automatic Classification of Single Facial Images,”IEEE Trans. on PAMI, Vol.21,No.12, December 1999.
- N. Krüger, M. Pötzsch and C. von der Malsburg, “Determining of Face on Deformable Graphs,” IEEE Trans. on Image Proc.,Vol.8, No.4, pp.504-515, 1999.
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