Reliable Face Recognition Using Artificial Neural Network

Reliable Face Recognition Using Artificial Neural Network

Shaimaa A. El-said (Department of Electronics and Communications, Zagazig University, Zagazig, Egypt)
Copyright: © 2013 |Pages: 29
DOI: 10.4018/ijsda.2013040102
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Facial detection and recognition are among the most heavily researched fields of computer vision and image processing. Most of the current face recognition techniques suffer when the noises affect the global features or the local intensity pixels of the images under consideration. In the proposed Reliable Face Recognition System (RFRS) system, for the first time, a combination of Gabor Filter (GF), Principal component analysis (PCA) for efficient feature extraction, and ANN for classification is employed. This demonstrates how to detect faces in noisy images by training the network several times on various input; ideal and noisy images of faces. Applying GF before PCA reduces PCA sensitivity to noise, provides a greater level of invariance, and trains the ANN on different sets of noisy images. The output of the ANN is a vector whose length equal to the distinct subjects in Olivetti Research Laboratory (ORL). The ANN is trained to output a 1 in the correct position of the output vector and to fill the rest of the output vector with 0’s. Experimentation is carried out on RFRS by using ORL datasets. The experimental results show that training the network on noisy input images of face greatly reduce its errors when it had to classify or recognize noisy images. For noisy face images, the network did not make any errors for faces with noise of mean 0.00 or 0.05, while the average recognition rate varies from 96.8% to 98%. When noise of mean 0.10 is added to the images the network begins to make errors. For noiseless face images, the proposed system achieves correct classification. Performance comparison between RFRS and other face recognition techniques shows that for most of the cases, RFRS performs better than other conventional techniques under different types of noises and it shows the high robustness of the proposed algorithm.
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1. Introduction

Over the past ten years, face recognition (FR) has received substantial attention from researchers in biometrics, pattern recognition, computer vision, and cognitive psychology communities (Hsu, 2002). FR is a difficult problem due to the general similar shape of faces combined with the numerous variations between images of the same face. Recognition of faces from an uncontrolled environment is a very complex task: lighting condition may vary tremendously; facial expressions also vary from time to time; face may appear at different orientations and a face can be partially occluded. Further, depending on the application, handling facial features over time (aging) may also be required. Although existing methods perform well under constrained conditions, the problems with the illumination changes, out of plane rotations and occlusions still remain unsolved. The area of face recognition is well described at present, e.g. starting by conventional approaches (PCA, LDA) (Turk & Pentland, 1991; Marcialis & Roli, 2002; Martinez & Kak, 2001), and continuing at present by kernel methods (Wang, et al., 2008; Hotta, 2008; Wang et al., 2004; Yang, 2002; Yang et al., 2005). Advances in face recognition are summarized also in books (Li & Jain, 2005; Delac et al., 2008) and book chapters (Oravec et al., 2008).

Environmental corruption such as noise, blur, adverse illumination and compression rates (in JPEG and other compression techniques) influence the performance of state-of-art recognition algorithms. Several enhancement methods have been proposed in literature to handle these corruptions (Chang, 2000). Using denoising techniques to enhance the quality of images will increase both complexity and computational cost besides that its performance depends on the type and quantity of noise. So, this paper aims to enhance the performance of recognition systems in noisy environment in simple manner without using denoising techniques. This is done by using a combination of GF with PCA for efficient feature extraction, and ANN that are trained in a special method for classification.

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