Age progression regression is a challenging task due to the complicated and non-linear transformation in human aging process. Many researches have shown that both global and local facial features are essential for face representation, but previous GAN based methods mainly focused on the global feature in age synthesis. To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN). In our generator, a global network learns the whole facial structure and simulates the aging trend of the whole face, while three crucial facial patches are progressed or regressed by three local networks aiming at imitating subtle changes of crucial facial subregions. To preserve most of the details in age-attribute-irrelevant areas, our generator learns the residual face. Moreover, we employ an identity preserving loss to better preserve the identity information, as well as age preserving loss to enhance the accuracy of age synthesis. A pixel loss is also adopted to preserve detailed facial information of the input face. Our proposed method is evaluated on three face aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset. Experimental results show appealing performance of the proposed method by comparing with the state-of-the-art.
Meanwhile, prototype-based approaches @cite_2 @cite_18 regard average faces of each age group as their prototypes, thus the aging pattern is the difference between prototypes of target and source age groups. However, prototype-based approaches may ignore personality, e.g., wrinkles. For this problem, @cite_25 proposed an age synthesis based on aging dictionary learning to reconstruct the aging face utilizing an aging dictionaries of different age groups.
@cite_18 @cite_25 @cite_2
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In this paper, we aim to automatically render aging faces in a personalized way. Basically, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each subject may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups. Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces. Transforming facial images along perceived dimensions (such as age, gender, race, or health) has application in areas as diverse as psychology, medicine, and forensics. We can use prototype images to define the salient features of a particular face classification (for example, European female adult or East-Asian male child). We then use the differences between two prototypes to define an axis of transformation, such as younger to older. By applying these changes to a given input face, we can change its apparent age, race, or gender. Psychological investigations reveal a limitation with existing methods that's particularly apparent when changing the age of faces. We relate the problem to the loss of facial textures (such as stubble and wrinkles) in the prototypes due to the blending process. We review the existing face prototyping and transformation methods and present a new, wavelet-based method for prototyping and transforming facial textures