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Recognition and segmentation of components of a face by a multi-resolution neural network

  • Part VI: Speech, Vision, and Pattern Recognition
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

This paper offers an artificial neural network that recognizes and segments a face and its components (e.g., eyes and mouth) from a complex background. The selective attention model (Fukushima, 1987) has been extended to have two channels of different resolutions. The high-resolution channel can analyze input patterns in detail, but usually lacks the ability to get global information because of small receptive fields of the cells in it. On the other hands, the low-resolution channel, whose cells have large receptive fields, can capture global information but only roughly. The proposed network analyses object by the interaction of both channels. Computer simulation has demonstrated that the network, which has learned only a small number of facial front views, can recognize and segment faces, eyes and mouths correctly from images containing a variety of faces against complex background.

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References

  1. Fukushima, K.: Neural network model for selective attention in visual pattern recognition and associative recall. Applied Optics 26[23] (1987) 4985–4992

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  2. Fukushima, K., Hashimoto, H.: Recognition and segmentation of components of a face with selective attention. Trans. MICE D-II, J80-D-II[8] (1997) in press

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  3. Fukushima, K.: Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks 1[2] (1988) 119–130

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Fukushima, K., Hashimoto, H. (1997). Recognition and segmentation of components of a face by a multi-resolution neural network. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020272

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  • DOI: https://doi.org/10.1007/BFb0020272

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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