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

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.




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.