الفهرس 
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Abstract
Security is becoming one of the main human concerns nowadays. The need for an identity that cannot be destroyed, forgotten or stolen is greatly recommended in a world. Hence, biometrics is taking an important role in personal identification system. The area of face recognition is closely related to biometrics. Face recognition is among the most heavily researched fields of computer vision and image processing. Face is a complex multidimensional structure and needs good computing techniques for recognition. Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Face Recognition is a computer based application which detects the faces of different persons for authentication and various other purposes. It has separate applications from the fingerprint and iris recognitions. In the face recognition approach, a given face is compared with the faces stored in a face database in order to identify the person. The purpose is to find a face in the database, which has the highest similarity with the given face. Most successful forms of the human surveillance are the facial recognition. In the fields of biometric, facial recognition technology is one of the fastest growing fields. Face recognition is a challenging issue due to variation in expression, pose, illumination and aging etc. In this thesis a hybrid combination of principal component analysis (PCA) and discrete cosine transform (DCT) is used to represent accurate face recognition system. In this thesis, we propose a simple and fast face recognition system on the basis of feature extraction using Principal Component Analysis (PCA) which is a classical and successful method of dimension reduction and the discrete cosine transform (DCT); a well know compression technique. This method proposes for improving the recognition rate of the face recognition system. Standard database such as FACES94 and AT&T are used to test the experimental results which proves that proposed system achieves more accurate face recognition as compared to individual methods. The main advantage of this technique is increased the efficiency, high speed and better recognition rate.