Abstract
We propose a novel neural network for classification of visual patterns. The new network, called pyramidal neural network or PyraNet, has a hierarchical structure with two types of processing layers, namely pyramidal layers and 1-D layers. The PyraNet is motivated by two concepts: the image pyramids and local receptive fields. In the new network, nonlinear 2-D are trained to perform both 2-D analysis and data reduction. In this paper, we present a fast training method for the PyraNet that is based on resilient back-propagation and weight decay, and apply the new network to classify gender from facial images.
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References
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Garcia, C., Delakis, M.: Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(11), 1408–1423 (2004)
Fukushima, K.: Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks 1(2), 119–130 (1988)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, New York (2002)
Riedmiller, M., Braun, H.: A direct adaptive method of faster backpropagation learning: The rprop algorithm. In: IEEE International Conference on Neural Networks, San Francisco, pp. 586–591 (1993)
Gray, M., Lawrence, D.T., Golomb, B.A., Sejnowski, T.J.: A perceptron revealing the face of sex. Neural Computation 7(6), 1160–1164 (1995)
Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)
Jain, A., Huang, J.: Integrating independent components and linear discriminant analysis for gender classification. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 159–163 (2004)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
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© 2006 Springer-Verlag Berlin Heidelberg
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Phung, S.L., Bouzerdoum, A. (2006). Gender Classification Using a New Pyramidal Neural Network. 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_23
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DOI: https://doi.org/10.1007/11893257_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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