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Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

Author(s): S. Sakthivel | M. Rajaram

Journal: Journal of Computer Science
ISSN 1549-3636

Volume: 7;
Issue: 3;
Start page: 366;
Date: 2011;
Original page

Keywords: Multi resolution analysis | support vector machine | wavelet transformation | Discrete Cosine Transformation (DCT) | Principal Component Analysis (PCA) | Linear Discriminant Analysis (LDA) | Multi-Dimensional Scaling (MDS) | Discrete Wavelet Transform (DWT) | Linear Discriminant Analysis (LDA) | Radial Basis Function (RBF)

Problem statement: Recognizing a face based attributes is an easy task for a human to perform; it is closely automated and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or a facial feature and must be programmed with an algorithm to do so. Generally, to recognize a face, different kinds of the facial features were used separately or in a combined manner. In the previous work, we have developed a machine learning based multi attribute face recognition algorithm and evaluated it different set of weights to each input attribute and performance wise it is low compared to proposed wavelet decomposition technique. Approach: In this study, wavelet decomposition technique has been applied as a preprocessing technique to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The Experiment was specifically designed to investigate the gain in robustness against illumination and facial expression changes. Results: In this study, a wavelet based image decomposition technique has been proposed to enhance the performance by 8.54 percent of the previously designed system. Conclusion: The proposed model has been tested on face images with difference in expression and illumination condition with a dataset obtained from face image databases from Olivetti Research Laboratory.
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