Occlusion the performance of the face recognition algorithms under occlusion is in general poor.

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Occlusion the performance of the face recognition algorithms under occlusion is in general poor. The face may be occluded by other objects in the scene or by sunglasses or other things. Occlusion may be unintentional or intentional. Under some conditions subjects may be motivated to thwart recognition efforts by covering portions of their face. Since in many situations, the goal is to recognize none or even un-cooperating subjects. Time delay Faces change over time. There are changes in hair style, makeup, muscle tension and appearance of the skin, presence or absence of facial hair, glasses, or facial jewellery, and over longer periods effects related to aging. Pose Some unavoidable problems appear in the variety of practical…show more content…
Face recognition under extreme facial expression still remains an unsolved problem, and temporal information can provide significant additional information in face recognition under expression. A neutral face is a relaxed face without contraction of facial muscles and without facial movements. Face recognition systems can achieve high recognition rate for good quality, frontal view, constant lighting and only subtle expression or expressionless face images.Therefore, it is important to automatically find the best face of a subject from the images. Using the neutral face during enrolment and when authenticating, so that we can find the neutral face of the subject from the six universal expression like. Happiness, sadness, disgust, anger, fear, surprise. Gender researchers found surprisingly consistent differences of face recognition rates related to gender. In two databases (AR and FERET) the recognition rate for female subjects is higher than for males across a range of perturbations. One hypothesis is that women invest more effort into modifying their facial appearance, by use of cosmetics, for instance, which leads to greater differentiation among women than men. Alternatively, algorithms may simply be more sensitive to structural differences between the faces of women and men. The finding that algorithms are more sensitive to women’s faces suggests that there may be other

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