Artificial Neural Network in Face Detection Human on Digital Image
- 1 Department of Computer Science, Northern Border University, Saudi Arabia
Method itself is proposed to be formed by series of filters. Each filter is an independent method of detection and allows you to cut off quickly the regions that do not contain the face’s areas. For this purpose some of the different characteristics of the object are used in addition each subsequent part processes only promising areas of image which were obtained from the previous parts of the method. It has been tested by means of CMU/MIT test set. Analogy of speed and quality detection. There are two modifications to the classic use of neural networks in face detection. First the neural network only tests candidate regions for the face, thus dropping the search space. Secondly the window size is used in network scanning the input image is adaptive and depends on the size of the region of the candidate are implemented in Using Mat lab. The analysis of detection quality of a new method in comparison with the algorithm. The experimental results show that the proposed method the detection method, based on rectangular primitives, in quality. The proposed method, tested on a standard Test set, has surpassed all known methods in speed and quality of detection. Our approach without pre-treatment is not required because the normalization is enabled directly in the weights of the input network.
Copyright: © 2013 Abdusamad Al-Marghilani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Artificial Neural Network Faces Detection
- Sifting Filter
- Cascade Model
- Classifying Primitives