Improving the Performance of Machine Learning based Face Recognition Algorithm with Multiple Weighted Facial Attribute Sets S.Sakthivel, Assistant Professor Department of IT, Sona College of Technology, Salem India sakvel75@gmail.com Dr.R.Lakshmipathi Professor, Department of EEE, St. Peter’s Engineering College, Chennai, India drrlakshmipathi@yahoo.com M.A.Manikandan Assistant Professor, Department of MCA, Sona College of Technology, Salem, India maniintouch@gmail.com Abstract Recognizing a face based on its attributes is an easy task for a human to perform; it is nearly automatic, and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or facial features, 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. Feature fusion methods and parallel methods performed by integrating multiple feature sets at different levels. However, these feature fusion methods as well as parallel methods do not guarantee better result. Several Feature extraction techniques and fusion models were explored in several earlier works. This work, addresses feature fusion model with multiple weighted facial attribute set. For facial feature set creation, 1. PCA based Eigen feature extraction technique, 2. DCT based feature extraction technique, 3. Histogram Based Feature Extraction technique and 4. Simple intensity based feature Extraction were used. The proposed model has been tested on face images which differ in expression and illumination condition with a dataset obtained from face image databases of ORL. A more significant improvement in term of accuracy was achieved and more significant results were arrived. Keywords: Biometrics, Feature Fusion. Parallel Methods, PCA, DCT, Histogram Matching, Weighted Facial Attribute. 1. Introduction Face recognition is an important part of today’s emerging biometrics and video surveillance markets. Face Recognition can benefit the areas of: Law Enforcement, Airport Security, Access Control, Driver’s Licenses & Passports, Homeland Defense, Customs & Immigration and Scene Analysis. Face recognition has been a research area for almost 30 years, with significantly increased research activity since 1990[15] [14]. This has resulted in the development of successful algorithms and the introduction of commercial products. But, the researches and achievements on face recognition are still in its initial stages of development. Although face recognition is still in the research and development phase, several commercial systems are currently available and research organizations are working on the development of more accurate and reliable systems. Using the present technology, it is impossible to completely model human recognition system and reach its performance and accuracy. However, the human brain has its shortcomings in some aspects. The benefits of a computer system would be its capacity to handle large amount of data and ability to do a job in a predefined repeated manner. The observations and findings about human face recognition system will be a good starting point for automatic face attribute analysis. 1.1 Early Works Face recognition has gained much attention in the last two decades due to increasing demand in security and law enforcement applications. Face recognition methods can be divided into two major categories, appearance-based method and feature- based method. Appearance-based method is more popular and achieved great success [2]. Appearance-based method uses the holistic features of a 2-D image [2]. Generally face images are captured in very high dimensionality, normally which is more than 1000 pixels. It is very difficult to perform face recognition based on original face image without reducing the dimensionality by extracting the important features. Kirby and Sirovich first used principal component analysis (PCA) to extract the features from face image and used them to represent human face image [15]. PCA seeks for a set of projection vectors which project the image data into a subspace based on the variation in energy. Turk and Pentland introduced the well-known Eigenface method [14]. Eigenface method incorporates PCA and showed promising results. Another well-known method is Fisher face. Fisher face incorporates linear discriminant analysis (LDA) 978-1-4244-4457-1/09/$25.00 '2009 IEEE 658