The Evaluation of Camera Motion, Defocusing and Noise Immunity for Linear Appearance Based Methods in Face Recognition

Face recognition has assigned a special place to itself because of its low intrusiveness, low cost and effort and acceptable accuracy. There are several methods for recognition and appearance based methods is one of the most popular one. Unfortunately most of the papers that have been published these years have just shown the results on the databases that are all without any noise and all of focus. But it is clear that for a real system all these problems can happen, so finding methods that are robust to such problems is important. In this paper we show that linear appearance based methods are robust to an acceptable degree to problems such as, when the camera is moving or it is defocus and when the image is influenced with Gaussian noise. For linear appearance based methods we chose Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Multiple Exemplar Discriminant Analysis (MEDA) that has shown better performance than other appearance based methods.