Abstract
This article addresses the problem of rotation invariant face detection using convolutional neural networks. Recently, we developed a new class of convolutional neural networks for visual pattern recognition. These networks have a simple network architecture and use shunting inhibitory neurons as the basic computing elements for feature extraction. Three networks with different connection schemes have been developed for in-plane rotation invariant face detection: fully-connected, toeplitz-connected, and binary-connected networks. The three networks are trained using a variant of Levenberg-Marquardt algorithm and tested on a set of 40,000 rotated face patterns. As a face/non-face classifier, these networks achieve 97.3% classification accuracy for a rotation angle in the range ±900 and 95.9% for full in-plane rotation. The proposed networks have fewer free parameters and better generalization ability than the feedforward neural networks, and outperform the conventional convolutional neural networks.
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References
Ampazis, N., Perantonis, S.J.: Two highly efficient second-order algorithms for training feedforward networks. IEEE Transactions on Neural Networks 13(5), 1064–1074 (2002)
Arulampalam, G., Bouzerdoum, A.: Application of shunting inhibitory artificial neural networks to medical diagnosis. In: Proc. of the Seventh Australian and New Zealand Intelligent Information Systems Conference, Perth, pp. 89–94 (2001)
Barnard, E., Casasent, D.: Invariance and neural nets. IEEE Transactions on Neural Networks 2, 498–508 (1991)
Bouzerdoum, A.: A new class of high-order neural networks with nonlinear decision boundaries. In: Proc. of the Sixth International Conference on Neural Information Processing, Perth, vol. 3, pp. 1004–1009 (1999)
Bouzerdoum, A.: Classification and function approximation using feed-forward shunting inhibitory artificial neural networks. In: Proc. of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp. 613–618 (2000)
Casasent, D., Psaltis, D.: Position, rotation and scale-invariant optical correlation. Applied Optics 15(7), 1795–1799 (1976)
Chong, C.-W., Raveendran, P., Mukundan, R.: Translation invariants of zernike moments. Pattern Recognition 36(8), 1765–1773 (2003)
Fukushima, K., Miyake, S., Ito, T.: Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transactions Systems, Man, and Cybernetics SMC-13(5), 826–834 (1983)
Grossberg, S. (ed.): Neural Networks and Natural Intelligence. MIT Press, Cambridge (1988)
Gruber, M., Hsu, K.Y.: Moment-based image normalization with high noise tolerance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 136–139 (1997)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions Information Theory IT-8, 179–187 (1962)
Khotanzad, A., Lu, J.H.: Classification on invariant image representations using a neural network. IEEE Transactions on Acoustics, Speech, and Signal Processing 38, 1028–1038 (1990)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4), 541–551 (1989)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. of the IEEE 86(11), 2278–2324 (1998)
Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 148–154 (2005)
Rodrigues, M.A.: Invariants for pattern recognition and classification. Machine perception and artificial intelligence, vol. 42. World Scientific, Singapore (2000)
Teague, M.: Image analysis via the general theory of moments. Journal of the Optical Society of America 70(8), 920–930 (1980)
Teh, C.H., Chin, R.T.: On image analysis by the method of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(4), 496–513 (1988)
Tivive, F.H.C., Bouzerdoum, A.: Efficient training algorithms for a class of shunting inhibitory convolutional neural networks. IEEE Transactions on Neural Networks 16(3), 541–556 (2005)
Wood, J.: Invariant pattern recognition: a review. Pattern Recognition 29(1), 1–17 (1996)
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Tivive, F.H.C., Bouzerdoum, A. (2006). Rotation Invariant Face Detection Using Convolutional Neural Networks. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_29
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DOI: https://doi.org/10.1007/11893257_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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