Face recognition under varying pose

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Abstract
Researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing human faces for the last 20 years. While some systems, especially template-based ones, have been quite successful on expressionless, frontal views of faces with controlled lighting, not much work has taken face recognizers beyond these narrow imaging conditions. Our goal is to build a face recognizer that works under varying pose, the difficult part of which is to handle face relations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. To recognize a novel view, the recognizer locates the eyes and nose features, uses these locations to geometrically register the input with model views, and then uses correlation on model templates to find the best match in the data base of people. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modeling views per person.

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