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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Biometric Face Recognition with Different Training and Testing Databases
verfasst von : Joan Fabregas, Marcos Faundez-Zanuy
Erschienen in: Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction
Verlag: Springer Berlin Heidelberg
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Biometric face recognition presents a wide range of variability sources, such as make up, illumination, pose, facial expression, etc. Although some public available databases include these phenomena, it is a laboratory condition far away from real biometric system scenarios. In this paper we perform a set of experiments training and testing with different face databases in order to reduce the wide range of problems present in face images from different users (make up, facial expression, rotations, etc.). We use a novel dispersion matcher, which opposite to classical biometric systems, does not need to be trained with the whole set of users. It can recognize if two photos are of the same person, even if the photos of that person were not used in training the classifier.