Handbook of image processing operators
Handbook of image processing operators
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Fast and Accurate Face Detector for Indexation of Face Images
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A New Robust Face Detection in Color Images
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A fast computerized method for automatic simplification of boolean functions
ISTASC'09 Proceedings of the 9th WSEAS International Conference on Systems Theory and Scientific Computation
Fast information processing over business networks
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
A new automated information retrieval system by using intelligent mobile agent
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Integrating neural networks and PCA for fast covert surveillance
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
Fast Karnough map for simplification of complex Boolean functions
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Fast human motion tracking by using high speed neural
SSIP '09/MIV'09 Proceedings of the 9th WSEAS international conference on signal, speech and image processing, and 9th WSEAS international conference on Multimedia, internet & video technologies
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
A new approach for prediction by using integrated neural networks
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
An intelligent approach for fast detection of biological viruses in DNA sequence
ACELAE'11 Proceedings of the 10th WSEAS international conference on communications, electrical & computer engineering, and 9th WSEAS international conference on Applied electromagnetics, wireless and optical communications
A multilevel information fusion approach for visual quality inspection
Information Fusion
A new expert system for pediatric respiratory diseases by using neural networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
An enhanced classifier fusion model for classifying biomedical data
International Journal of Computational Vision and Robotics
Surveillance of video signals over computer networks
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
Real-time transmission of video streaming over computer networks
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
A new hybrid system for information security
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
Multi-metric learning for multi-sensor fusion based classification
Information Fusion
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Neural networks have shown good results for detecting a certain pattern in a given image. In this paper, efficient neural networks (ENNs) for face detection are presented. Such classifiers are designed based on cross-correlation in the frequency domain between the input matrix and the input weights of neural networks. This approach is developed to reduce the computation steps required by these ENNs for the searching process. The principle of divide and conquer strategy is applied through matrix decomposition. Each matrix is divided into smaller in size sub-matrices and then each one is tested separately by using a single efficient neural classifier. Then, the final result is achieved by fusing results from the combined classifiers. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulting sub-matrices at the same time using the same number of ENNs. Another advantage is that the speed up ratio is increased with the size of the input matrix when using ENNs and matrix decomposition. In addition, for many reasons presented here, it is proved that the equations given in previous work [S. Ben-Yacoub, B. Fasel, J. Luettin, Fast face detection using MLP and FFT, in: Proceedings of the Second International Conference on Audio and Video-based Biometric Person Authentication (AVBPA'99), 1999; B. Fasel, Fast multi-scale face detection, IDIAP-Com 98-04, 1998; S. Ben-Yacoub, Fast object detection using MLP and FFT, IDIAP-RR 11, IDIAP, 1997] for conventional and fast neural networks are not valid. Correct equations for the number of computation steps required by cross-correlation in the spatial and frequency domains are given. Moreover, the problem of local sub-matrix normalization in the frequency domain is solved. The effect of matrix normalization on the speed up ratio of face detection is discussed. Simulation results show that local sub-matrix normalization through weight normalization is faster than sub-matrix normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.