This project is mainly focussed on the recognition of face for security purpose. But it could also be used to obtain quick access to medical, criminal, or any type of records. Solving this problem is important because it could allow personnel to take preventive action, provide better service - in the case of a doctor’s appointment, or allow a person access to a secure area. This project work is proposed to design using MATLAB. In this project we are using PCA algorithms for saving database of image i.e. different expressions of face and detection of the face for security reasons. A face recognition system generally consists of four modules as face localization, normalization, feature extraction, and matching. There was a dormant period in automatic face recognition until the work by Sir Ovich and Kirby on a low dimensional face representation. This was derived using the Karhunen–Loeve Transform or Principal Component Analysis (PCA). It is the pioneering work of Turk and Pentl and on Eigen-face that reinvigorated face recognition research. Other major milestones in face recognition include: the Fisher face method, which applied Linear Discriminate Analysis (LDA). LDA method is used after a PCA step to achieve higher accuracy. Local filters such as Gabor jets are used to provide more effective facial features. The design of the Adaboost learning based cascade classifier architecture is used for real time face detection.
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