Face recognition using eigenfaces
Abstract : We tried to develop a real time face detection and recognition system which uses an “appearance-based” approach. For detection purpose we used Viola Jones algorithm. To recognize face we worked with Eigen Faces which is a PCA based algorithm. In a real time to recognize a face we need a data training set. For data training set we took five images of each person and manipulated the Eigen values to match the known individual.
? Facial features are removed and implemented through algorithms which are proficient and some notifications are done to improve the existing algorithm models.
? When compared to previous existing algorithm the proposed algorithm takes less time to run the program.
? Efficient face recognition by using Eigen face approach is presented.
? The proposed protected system is able to perform user recognition.
? To evaluate the reasonability of this way to deal with face recognition, we created an example set of face pictures with certain varieties of lighting and direction.
? Indexing schemes and other techniques were developed to cope up with these problems.
? Many problems there are a number of interesting features in Eigenface method.
? It solves the problem by maximizing the ratio between class scatter to within-class scatter.
? It can be more problematic when there are extreme changes in the pose, expression or the person is in disguise.
? These problems decrease the efficiency of the system. These problems can be somewhat manageable but not totally avoidable.
• An unsupervised pattern recognition scheme is proposed in this paper which is independent of excessive geometry and computation.
• The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database.
• The proposed technique is coding and decoding of face images, emphasizing the significant local and global features.
• The proposed method is independent of any judgment of features (open/closed eyes, different facial expressions, with and without Glasses).
• The proposed technique is analyzed by varying the number of eigenfaces used for feature extraction.
? Eigenfaces is a crucial component for the performance of a facial recognition system.
? But the background can change in the real world scenarios; this will hamper the recognition performance.
? Eigen Faces are used for: i) to get the appropriate facial info and ii) Efficiently produce facial image.
? To get related information in facial image, we need to encode it efficiently and relate the face with a dataset of images encoded in the same way.
? To learn and recognize faces of the preferred personal, building up the characteristic features by practice is an efficient way.
? when the dimensions of the face space is smaller than the number of face classes only that time the recognition system is efficient.
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