11/14/2022 0 Comments Illumination aware age progression![]() ![]() To achieve flexible bidirectional age changes, it may need to retrain the model inversely. In addition, these approaches only provide age progression from younger face to older ones. ![]() All prototype-based approaches perform the group-based learning which requires the true age of testing faces to localize the transition state which might not be convenient. The deep learning-based method represents the state-of-the-art, where RNN is applied on the coefficients of eigenfaces for age pattern transition. However, this approach presents serious ghosting artifacts. The age pattern was transited to the target age pattern through the sub-dictionaries, and then the aged face is generated by synthesizing the personal pattern and target age pattern. A given face will be decomposed into two parts: age pattern and personal pattern. To preserve the personality, proposed a dictionary learning based method - age pattern of each age group is learned into the corresponding sub-dictionary. However, the aged face generated from averaged prototype may lose the personality (e.g., wrinkles). Then, the difference between prototypes from two age groups is considered the aging pattern. On the other hand, prototype-based approaches often divide faces into groups by age, e.g., the average face of each group, as its prototype. We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination. In addition, physical modeling-based approaches are computationally expensive. However, in order to better model the subtle aging mechanism, it will require a large face dataset with long age span (e.g., from 0 to 80 years old) of each individual, which is very difficult to collect. through either parametric or non-parametric learning. Physical model-based methods model the biological pattern and physical mechanisms of aging, e.g., the muscles, wrinkle, facial structure etc. Age Progression and Regression In recent years, the study on face age progression has been very popular, with approaches mainly falling into two categories, physical model-based and prototype-based. ![]()
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