Handbook of image processing operators
Handbook of image processing operators
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Iris Detection for Personal Verification Using Modular Neural Nets
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Fast Face Detection Using Neural Networks and Image Decomposition
AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology
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
Fast Modular Neural Nets for Human Face Detection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
A New Robust Face Detection in Color Images
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
EURASIP Journal on Applied Signal Processing
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Finding an object or a face in the input image is a search problem in the spatial domain. Neural networks have shown good results for detecting a certain face/object in a given image. In this paper, faster neural networks for face/object detection are presented. Such networks are designed based on cross correlation in the frequency domain between the input image and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the searching process. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size subimages and then each one is tested separately by using a single faster neural network. Furthermore, fastest face/object detection is achieved by using parallel processing techniques to test the resulting sub-images at the same time using the same number of faster neural networks. In contrast to using only faster neural networks, the speed up ratio is increased with the size of the input image when using faster neural networks and image decomposition. Moreover, the problem of local subimage normalization in the frequency domain is solved. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.