Abstract
Two methods for recognizing face images have been implemented and compared. The methods are edge map, and Eigenfaces. Various obstacles like changing illumination, facial expression, pose, and background greatly affect the performance of the methods. The effects of these parameters on these two methods have been studied. To achieve this, different databases containing face images obtained under different conditions, have been used. The databases used were face94 and grimace provided by Dr. Libor Spacek from the University of Essex UK and the Yale B extended database from Yale University. Matlab was used to execute the codes and implement the algorithms of the two methods. After conducting numerous face-matching tests, it has been concluded that edge map using M2HD is more robust when it comes to variation in illumination and works considerably well with severe changes in facial expression and pose. However, at the same time it is computationally very expensive. On the other hand, Eigenfaces method, using principal component analysis (PCA) is fast, less complex, and requires very little storage space. Nevertheless, performance under variations in illumination is poor.