Research Article Open Access

Artificial Neural Network in Face Detection Human on Digital Image

Abdusamad Al-Marghilani1
  • 1 Northern Border University, KSA
American Journal of Applied Sciences
Volume 10 No. 10, 2013, 1234-1239

DOI: https://doi.org/10.3844/ajassp.2013.1234.1239

Submitted On: 28 July 2013 Published On: 6 September 2013

How to Cite: Al-Marghilani, A. (2013). Artificial Neural Network in Face Detection Human on Digital Image. American Journal of Applied Sciences, 10(10), 1234-1239. https://doi.org/10.3844/ajassp.2013.1234.1239

Abstract

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.

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Keywords

  • Artificial Neural Network Faces Detection
  • Sifting Filter
  • Classifier
  • Cascade Model
  • Classifying Primitives