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A Unified Robust Algorithm for Detection of Human and Non-human Object in Intelligent Safety Application

Authors: M A Hannan, A. Hussain, S. A. Samad, K. A. Ishak, A. Mohamed

Abstract:

This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object.

Keywords: Algorithm, detection of human and non-human object, FNN, CNN, Image training.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075719

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References:


[1] S. Mahamud and M. Hebert, "The Optimal Distance Measure for Object Detection" Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-03), 2003, Vol. 1, pp. 248-255.
[2] A. Mohan, C. Papageorgiou, and T. Poggio, "Example-Based Object Detection in Images by Components", IEEE Transaction on Pattern Analysis and machine Intelligence, 2001.Vol. 23, No. 4, pp. 349-361.
[3] S. Ullman, E. Sali, and M. Vidal-Naquet, "A Fragment-Based Approach to Object Representation and Classification", Proc. Fourth Int-l Workshop Visual, 2001, pp. 85-100.
[4] S. Ullman, M. Vidal-Naquet, and E. Sali, "Visual Features of Intermediate Complexity and Their Use in Classification", Nature Neuroscience, 2002, Vol. 5, No. 7, pp. 682-687.
[5] M. Weber, M. Welling and P. Perona, "Unsupervised Learning of Models for Recognition", Proc. Sixth European Conf. on Computer Vision, 2000, pp. 18-32.
[6] D. Roth, M-H. Yang and N. Ahuja, "Learning to Recognize Three- Dimensional Objects," Neural Computation, 2002, Vol. 14, No. 5, pp. 1071-1103.
[7] S. Agarwal, A. Awan, and D. Roth, "Learning to Detect Objects in Images via a Sparse, Part-Based Representation", IEEE Transaction on Pattern Analysis and Machine Intelligence, 2004, Vol. 26, No. 11, pp. 1475-1490.
[8] W. Wang, Y. Gao, S. C. Hui and M. K. Leung, "A Fast and Robust Algorithm for Face Detection and Localization", Proc. of the 9th International Conference on Neural information Processing (ICONIP'02), 2002, Vol. 4, pp. 2118-2121.
[9] J. Colmenarez and T. S. Huang, "Face Detection With Information- Based Maximum Discrimination", IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 782-787.
[10] B. Moghaddam and A. Pentland, "Probabilistic Visual Learning for Object Representation", IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997, Vol. 19, No. 7, pp. 696-710.
[11] H. A. Rowley, S. Baluja, and T. Kanade, "Neural Network-Based Face Detection", IEEE Transaction on Pattern Analysis and Machine Intellignce, 1998, Vol. 20, No. 1, pp. 23-38.
[12] K-K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection", IEEE Transaction on Pattern Analysis and Machine Intelligence, 1998, Vol. 20, No. 1, pp. 39-51.
[13] H. M. El-bakry, "Fast Cooperative Modular Neural Nets for Human Face Detection", Proc. of IEEE International Conference on Image Processing, 7-10 Oct.,2001, Thessaloniki, Greece, pp. 1002-1005.
[14] L-L. Huang, A. Shimizu, Y. Hagihara and H. Kobatake, "Face detection from clustered images using polynomial neural network", Proceedings of the IEEE International Conference on Image Processing, 2001, pp. 669-672.
[15] E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: An application to face detection", Proc. of Computer Vision and Pattern Recognition, 1997, pp. 130-136.
[16] C. Papageorgiou and T. Poggio, "A trainable system for object detection", International Journal of Computer Vision, 2000, Vol. 38, No. 1, pp. 15-33.
[17] P. Viola and M. Jones, "Rapid object detection using a boosted cascacd of simple features", Proc. Computer Vision and Pattern Recogntion, 2001, Vol. 1, pp. 511-518.
[18] R. Crane, "A Simplified Approach to Image Processing", Prentice Hall, 1997.
[19] S. Ben-Yacoub, B. Fasel and J. Luettin, "Fast Face Detection using MLP and FFT", Proc. Second International Conf. On Audio and Video-based Biometric Person Authentication (AVBPA -99), 1999.
[20] B. Fasel, S. Ben-Yacoub and J. Luettin, "Fast Multi-Scale Face Detection", IDIAP-Com 98-04, 1998, pp. 1-87.