Abstract
A novel approach to critical parts of face detection problems is given, based on analogic cellular neural network (CNN) algorithms. The proposed CNN algorithms find and help to normalize human faces effectively while their time requirement is a fraction of the previously used methods. The algorithm starts with the detection of heads on color pictures using deviations in color and structure of the human face and that of the background. By normalizing the distance and position of the reference points, all faces should be transformed into the same size and position. For normalization, eyes serve as points of reference. Other CNN algorithm finds the eyes on any grayscale image by searching characteristic features of the eyes and eye sockets. Tests made on a standard database show that the algorithm works very fast and it is reliable.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
L.O. Chua and L. Yang, “Cellular neural networks: Theory,” IEEE Trans. on Circuits and Systems, Vol. 35, pp. 1257–1290, 1988.
L.O. Chua and T. Roska, “The CNN paradigm,” IEEE Trans. on Circuits and Systems I, Vol. 40, pp. 147–156, 1993.
F. Werblin, T. Roska and L.O. Chua, “The analogic cellular neural network as a bionic eye,” Journal of Circuit Theory and Applications, Vol. 23, pp. 541–569, 1995.
T. Roska, “CNN chip set architectures and the visual mouse,” IEEE Int. Workshop CNNA '96, Sevilla, pp. 487–492, 1996.
L.O. Chua and T. Roska, “Cellular neural networks: Foundations and primer,” Lecture notes for curse EE129 at U.C.Berkeley, 1997.
T. Roska and L.O. Chua, “The CNN universal machine: An analogic array computer,” IEEE Trans. on Circuits and Systems II, Vol. 40, pp. 163–173, 1993.
S.Y. Kung, “Neural networks for intelligent multimedia processing,” Proc. IEEE, Vol. 86, No. 6, pp. 1244–1272, 1998.
S.-H. Lin, S.Y. Kung, and L.-J. Lin, “Face recognition/ detection by probabilistic decision-based neural network,” IEEE Trans. on Neural Networks, Vol. 8, No. 1, pp. 114–132, 1997.
T. Kanade, “Picture processing by computer complex and recognition of human faces,” Technical Report, Kyoto University, Dept. of Information Science, 1973.
N. Intrator, D. Reisfeld, and Y. Yeshurun, “Face recognition using a hybrid supervised/unsupervised neural network,” Patt. Recog. Lett., Vol. 17, pp. 67–76, 1996.
P. Penev and J. Atick, “Local Feature Analysis: A general statistical theory for object representation,” Network: Comp. Neural Sys., Vol. 7, pp. 447–500, 1996.
I. Szatmári and T. Roska, “The CNN implementation of wave type metric for image analysis and classification,” IEEE Int. Workshop on Nonlinear Dynamics of Electronic Systems (NDES'98), Budapest, pp. 137–140, 1998.
A.H. Gee and R. Cipolla, “Fast visual tracking by temporal consensus,” Univ. Cambridge, Dept. Engineering Report TR 207, 1995.
C.H. Lee, J.S. Kim, and K.H. Park, “Automatic human face location in a complex background using motion and color information,” Patt. Recog., Vol. 29, pp. 1863–1876, 1996.
E. Littmann and H. Ritter, “Adaptive color segmentation: A comparison of neural and statistical methods,” IEEE Trans. Neural Networks, Vol. 8, pp. 175–185, 1997.
T. Roska, A. Zarándy, and L.O. Chua, “Color image processing using CNN,” Proc. Eur. Conf. on Circuit Theory and Design, Part I, Elsevier, Amsterdam, pp. 57–62, 1993.
T. Roska, L. Kék, L. Nemes, Á. Zarándy, M. Brendel, and P. Szolgay, (Eds.), “CNN software library” (Templates and Algorithms) Version 7.2, DNS-CADET-15, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, 1998.
M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognitive Neuroscience, Vol. 3, pp. 71–86, 1991.
K.K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” AI Memo 1521/Cbcl, MIT, Cambridge, MA, 1994.
P.J. Philips and Y. Bardi, “Efficient illumination normalisation of facial images,” Patt. Recog. Letters, Vol. 17, pp. 921–927, 1996.
M. Csapody and T. Roska, “Adaptive histogram equalization with CNN,” IEEE Int. Workshop CNNA '96, Sevilla, pp. 81–86, 1996.
G. Linán, S. Espejo, R.D. Castro, E. Roca, and A. Rodrigez-Vázquez, “CNNUC3: A mixed-signal 64 × 64 CNN universal chip,” Int. Conf. on Microelectronics for Neural, Fuzzy, and Bio-Inspirated Systems (MicroNeuro99), Granada, pp. 61–68, 1999.
S. Gutta, J. Huang, B. Takacs, and H. Wechsler, “Face recognition using ensembles networks,” Int. Conf. on Pattern Recognition, Vienna, Austria, 1996.
M.S. Lew, “Information theoretic view-based and modular face and modular face detection,” Proceedings of the Second International Conference on Automatic Face and Gesture Recognition (FG96), 1996.
B. Scassellati, “Finding eyes and faces with foveated vision system,” AAAI, 1998.
H. Rowley, S. Baluja and T. Kanade, “Human face detection in visual scenes,” CMU-CS-95-158, Carnegie Mellon University, 1995.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Balya, D., Roska, T. Face and Eye Detection by CNN Algorithms. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 23, 497–511 (1999). https://doi.org/10.1023/A:1008121908145
Published:
Issue Date:
DOI: https://doi.org/10.1023/A:1008121908145