An Expression Transformation for Improving the Recognition of Expression-Variant Faces From One Sample Image Per Person

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It is known that when only one sample image per gallery person is available, as a result of the small sample size problem, the recognition performance of discriminant feature extraction methods substantially degrades. This is particularly the case when the images are under drastic facial expression variation. To address this problem, this paper introduces an appearance-based expression transformation method to synthesize new expression images from the probe image. By feeding the synthesized images to discriminant feature extractor, a more robust recognition of gallery images with single sample image is achieved. The effectiveness of the proposed transformation method is demonstrated using the Cohn-Kanade facial expression database.