Sliding Window for Radial Basis Function Neural Network Face Detection

Mohamood, Nadzrie and Hashim, Nik Mohd Zarifie and Aziz, Khairul Azha A (2014) Sliding Window for Radial Basis Function Neural Network Face Detection. International Journal of Science and Engineering Applications , 3 (4). pp. 94-97. ISSN 2319-7560

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This paper present a Radial Basis Function Neural Network (RBFNN) face detection using sliding windows. The system will detect faces in a large image where sliding window will run inside the image and identified whether there is a face inside the current window. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBFNN offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the system will be based on the rate of detection and also false negative rate.

Item Type: Article
Uncontrolled Keywords: face detection, radial basis function neural network, sliding window
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering Technology > Department of Electronics & Computer Engineering Technology
Depositing User: Khairul Azha A Aziz
Date Deposited: 21 Jan 2015 02:28
Last Modified: 02 Sep 2021 14:32
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