Face detection and recognition system using principal component analysis

ABSTRACT Man machine interaction is a research area that has been focusing on enhancing computer support to humans by mimicking their human abilities. Computers can provide support starting from the daily life task to highly skilled job tasks. A good example for such a task is automated face recognition that could more efficiently be performed by computers. This thesis presents face detection and recognition system using PCA (Principal Component Analysis) algorithm with Eigenfaces technique. Eigenfaces is a fast and simple method for face recognition which is considered as a remarkable feature for it. Proposed approach in this thesis intent to implement PCA based technique with Eigenface approach for detection and tracking to enhance the results of face recognition in different head position, face profile and head scale. This will be implemented with a data set that have only five images for each individual, images will have unconstrained background for different environments for any given image to find the face and recognize the person in the image, where unconstrained background increase the complexity of the detection and recognition process, that requires these condition must be solved for real time implementation. Results have been showed that increasing number of training images can improve accuracy of the system. The number of training images per each individual, which is five for each sub-set group found as the optimal choice training for this approach. Comparison with standard dataset of faces has been done for verifying proposed work assumption; results were fair and approving for developed work with good accuracy of detection and recognition. Keywords: Eigenfaces, Face Detection, Face Recognition, Principal Component Analysis.