This paper proposes a new method to compare similarities of candidate models that are fitted to different areas of a query image. This method extracts the discriminant features that are changed due to the varying pose/lighting condition of given query image, and the confidence of each model-fitting is evaluated based on how much of the discriminant features is captured in each foreground. The confidence is fused with the similarity to enhance the face-identification performance. In an experiment using 7,000 images of 200 subjects taken under largely varying pose and lighting conditions, our proposed method reduced the recognition errors by more than 25% compared to the conventional method.
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