Impact of facial expressions and posture variations in face recognition rate on different image databases

In this paper, the impact of facial expression and posture variations in face recognition were studied by using three face recognition algorithms that are Eigenfaces, Fisherfaces, and LBPH in terms of recognition accuracy. In order to find the type of algorithms that works efficiently for face recognition in video. The experiment was conducted by using two different databases with three amounts of image in training set. DB-one is uncontrolled people in the images, and DB-two is controlled facial expressions and posture. The results show the facial expression and posture variations have a lot of impact to Eigenfaces and Fisherfaces and the LBPH got the impact less than the others. It concluded that the impact of facial expression and postures are different on each algorithm, and impacted to the recognition accuracy.