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Gender recognition is important for many applications including human computer interaction (HCI). This paper shows that gender recognition accuracy is affected significantly by the age of the person. Our empirical studies on a large face database of 8,000 images with ages from 0 to 93 years show that gender classification accuracy on adult faces can be 10%(More)
Biometrics is increasingly important in security applications. Iris recognition provides the greatest accuracy among known biometrics. The accuracy of iris recognition is, for example, much greater than face recognition and fingerprint recognition. However, it is not trivial to capture iris images in practice, and usually the users need to adjust their eye(More)
A new representation of faces, called face cyclographs, is introduced for face recognition that incorporates all views of a rotating face into a single image. The main motivation for this representation comes from recent psychophysi-cal studies that show that humans use continuous image sequences in object recognition. Face cyclographs are created by(More)
Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children’s development,(More)
Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in(More)
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