The Application Of Artificial Neural Network To Detect The Position Of Human Face In Digital Image
DOI:
https://doi.org/10.31642/JoKMC/2018/010801Keywords:
Face Detection, Artificial Neural Network, Quickprop, BackPropagation, Active LearningAbstract
One of the important parts in human face recognition is detecting face position. In this paper a implement face position detection experiment using (Artificial Neural Network, ANN) to give outputs of human face number, position and dimension as found in a digital image. The system is trained using available ace samples. Quickprop algorithm and active learning method are used to speed the system training process up. And also indicate the comparison of the training time with standard Backpropagation algorithm and the training with Quickprop algorithm. The experiment is conducted using 200, 300 and 400 data. For each of the trainings, the iteration is stopped when the error value reaches 0.05. It is observed that the bigger number of the training data of the Quickprop algorithm causes significant increase in the training rate. Based on the results of the experiment with 14 files containing 273 face images, the face detection system gives 70.24% detection rate and 62 false positives.
Downloads
References
S. E., Fahlman, 1988, “ An empirical study of learning speed in back-propagation networks”, (Tech. Rept. No. CMU-CS-88-162). Pittsburgh, PA: Carnegie Mellon University, pp: 88-162,
URL http://www.cs.cmu.edu/afs/cs.cmu.edu/user/sef/www/publications/qp-tr.ps.
R. Battiti, 1989, “Accelerated backpropagation learning: Two optimization methods”, Complex Systems, Vol.(3), pp: 331–342.
A. C., Veitch, G., Holmes, 1991, “A modified quickprop algorithm”, Neural Computat Vol. (3), pp:310–311. DOI: https://doi.org/10.1162/neco.1991.3.3.310
W. Schiffmann, M. Joost, R. Wernor, 1993, “Comparison of Optimized Backpropagation Algorithms”, Proc. Of the European Symposium on Artificial Networks (ESANN) ’93 Brussels.
H. Rowley, S. Baluja, T. Kanade, 1998, “Neural Network-Based Face Detection”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. (20), no. (1). DOI: https://doi.org/10.1109/34.655647
A., Nikov, S., Stoeva, 2001, “Quick fuzzy backpropagation algorithm”, Neural Network Vol. (14), pp:231–244. DOI: https://doi.org/10.1016/S0893-6080(00)00085-X
*7+ E., HjeLmas, B. k., Low, 2001, “Face Detection: A Survey “, Computer Vision and Image Understanding, No. (83), pp: 236-274. DOI: https://doi.org/10.1006/cviu.2001.0921
R., Caruana, S., Lawrence, and L., Giles, 2001, “Over fitting in Neural Networks: Backpropagation, Conjugate Gradient, and Early Stopping.”, In Advances in Neural Information Processing Systems, Denver, Colorado, MIT Press, Vol. (13), pp: 402-408.
M. H., Yang, D.J., Kriegman, and N., Ahuja, 2002, “Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24 (1), pp: 34-58. DOI: https://doi.org/10.1109/34.982883
M. Shin-yo, U. Haruki, 2003 “Face Detection and Facial Feature Localization for Human-machine Interface”, NII Journal No. (5), pp: 25-39.
S. Wiegand, C. Igel, and U. Handmann, 2004 “Evolutionary Multi-Objective Optimization Of Neural Networks For Face Detection”, International Journal of Computational Intelligence and Applications, World Scientific Publishing Company, Vol. (4), No. (3), pp: 1-16.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 Salman Abd Kadum
This work is licensed under a Creative Commons Attribution 4.0 International License.
which allows users to copy, create extracts, abstracts, and new works from the Article, alter and revise the Article, and make commercial use of the Article (including reuse and/or resale of the Article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made and the licensor is not represented as endorsing the use made of the work.
