Facial Expression Recognition using Principal Component Analysis Combined with Fisher Linear
Nagaraj N Bhat, Sumangala Bhavikatti · 2012
Human facial expressions play an important role in interpersonal relations. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machines must be taught to understand facial gestures. In this paper, we develop a facial expression recognition algorithm which is insensitive to variation in lighting direction and facial expression. Taking a pattern classification approach, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's Linear Discriminant (FLD) and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique (PCA), another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Euclidean distances between the projected test image and the projection of all centered training images were calculated. Test image is supposed to have minimum distance with its corresponding image in the training database. The algorithm is tested in MATLAB. Experimental results were tested on databases like JAFEE, Cohn-Kanade and with our own images.