Abstract
We propose a model for face recognition using a support vector machine being fed with a feature vector generated from outputs in several modules in bottom as well as intermediate layers of convolutional neural network (CNN) trained for face detection. The feature vector is composed of a set of local output distributions from feature detecting modules in the face detecting CNN. The set of local areas are automatically selected around facial components (e.g., eyes, moth, nose, etc.) detected by the CNN. Local areas for intermediate level features are defined so that information on spatial arrangement of facial components is implicitly included as output distribution from facial component detecting modules. Results demonstrate highly efficient and robust performance both in face recognition and in detection as well.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Belhumeur, P., Hesoanha, P., Kriegman, D.: Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. on Pattern Analysis and Machine Intelligence 15, 1042–1052 (1993)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conf. On Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Fukushima, K.: Neocognitron: a self-organizing neural networks for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36, 193–202 (1980)
Guodong, G., Li, S., Kapluk, C.: Face recognition by support vector machines. In: Proc. IEEE International Conf. On Automatic Face and Gesture Recognition, pp. 196–201 (2000)
Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: global versus component-based approach. In: Proc. International Conf. on Computer Vision, pp. 688–694 (2001)
Heisele, B., Koshizen, T.: Components for Face Recognition. In: Proc. IEEE International Conf. on Automatic Face and Gesture Recognition (2004)
Le Cun, Y., Bengio, T.: Convolutional networks for images, speech, and time series. In: Arbib, M.A. (ed.) The handbook of brain theory and neural networks, pp. 255–258. MIT Press, Cambridge (1995)
Li, Y., Gong, S., Liddel, H.: Support vector regression and classification based multi-view face detection and recognition. In: Proc. IEEE International Conf. on Automatic Face and Gesture Recognition, pp. 300–305 (2000)
Matsugu, M., Mori, K., Ishii, M., Mitarai, Y.: Convolutional spiking neural network model for robust face detection. In: Proc. International Conf. on Neural Information Processing, pp. 660–664 (2002)
Mitarai, Y., Mori, K., Matsugu, M.: Robust Face Detection System Based on Convolutional Neural Networks Using Selective Activation of Modules (In Japanese). In: Proc. Forum in Information Technology, pp. 191–193 (2003)
Moghaddam, B., Wahid, W., Pentland, A.: Beyond eigenfaces: probabilistic matching for face recognition. In: Proc. IEEE International Conf. on Automatic Face and Gesture Recognition, pp. 30–35 (1998)
Pontil, M., Verri, A.: Support vector machines for 3-d object recognition. IEEE Trans.on Pattern Analysis and Machine Intelligence 20, 637–646 (1998)
Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matsugu, M., Mori, K., Suzuki, T. (2004). Face Recognition Using SVM Combined with CNN for Face Detection. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive