Abstract
One of the major parts in human-computer interface applications, such as face recognition and video-telephony, consists in the localization of a face in an image. I propose to use hierarchical neural networks with local recurrent connectivity to solve this task, even in presence of complex backgrounds, difficult lighting, and noise. The network is trained using a database of gray-scale still images and manually determined eye coordinates. It is able to produce reliable and accurate eye coordinates for unknown images by iteratively refining an initial solution. Since the network processes an entire image, no time consuming scanning across positions and scales is needed. Its fast update allows for real-time face tracking.
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References
Behnke, S.: Learning iterative image reconstruction in the Neural Abstraction Pyramid. I.J. Computational Intelligence and Applications 1(4), 427–438 (2001)
Behnke, S.: Hierarchical Neural Networks for Image Interpretation. LNCS (LNAI), vol. 2766. Springer, Heidelberg (2003) (to appear)
Behnke, S., Rojas, R.: Neural Abstraction Pyramid: A hierarchical image understanding architecture. In: Proceedings IJCNN 1998, pp. 820–825 (1998)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Tr. on Neural Networks 5(2), 157–166 (1994)
Fukushima, K., Miyake, S., Ito, T.: Neocognitron: A neural network model for visual pattern recognition. IEEE Trans. SMC 13, 826–834 (1983)
Hjelmas, E., Low, B.K.: Face detection: A survey. Computer Vision and Image Understanding 83, 236–274 (2001)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. In: Int. Conf. on Audio-and Video-based Biometric Person Authentication, pp. 90–95 (2001)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. of IEEE 86(11), 2278–2324 (1998)
Maio, D., Maltoni, D.: Real-time face localization on gray-scale static images. Pattern Recognition 33, 1525–1539 (2000)
Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. Int. Conf. on Audio and Video-based Biometric Person Authentication, 72–77 (1999)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: Proceedings of ICNN 1993, pp. 586–591 (1993)
Rowley, H.A., Baluja, S., Kanade, T.: Neural network based face detection. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 23–38 (1998)
Williams, R.J., Peng, J.: An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Computation 2(4), 491–501 (1990)
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Behnke, S. (2003). Face Localization in the Neural Abstraction Pyramid. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_20
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DOI: https://doi.org/10.1007/978-3-540-45226-3_20
Publisher Name: Springer, Berlin, Heidelberg
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