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P., “ Least squares generative adversarial networks,” in Proc.
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K., “ Longitudinal study of automatic face recognition,” IEEE Trans. H., “ Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset,” IEEE Trans. and Tesafaye T., “ MORPH: A longitudinal image database of normal adult age-progression,” in Proc. Li P., Hu Y., Li Q., He R., and Sun Z., “ Global and local consistent age generative adversarial networks,” in Proc.Liu S., Sun Y., Wang W., Bao R., Zhu D., and Yan S., “ Face aging with contextural generative adversarial nets,” in Proc.G., Luu K., Le N., and Savvides M., “ Temporal non-volume preserving approach to facial age-progression and age-invariant face recognition,” in Proc. Zhang Z., Song Y., and Qi H., “ Ageprogression/regression by conditional adversarial autoencoder,” in Proc.Wang W., Cui Z., Yan Y., Feng J., Yan S., Shu X., and Sebe N., “ Recurrent face aging,” in Proc.D., “ Longitudinal face modeling via temporal deep restricted boltzmann machines,” in Proc. K., “ Learning face age progression: A pyramid architecture of GANs,” in Proc. Yang H., Huang D., Wang Y., and Jain A.Shu X., Tang J., Lai H., Liu L., and Yan S., “ Personalized age progression with aging dictionary,” in Proc.Yang H., Huang D., Wang Y., Wang H., and Tang Y., “ Face aging effect simulation using hidden factor analysis joint sparse representation,” IEEE Trans.M., “ Illumination-aware age progression,” in Proc. Kemelmacher-Shlizerman I., Suwajanakorn S., and Seitz S.Wang Y., Zhang Z., Li W., and Jiang F., “ Combining tensor space analysis and active appearance models for aging effect simulation on face images,” IEEE Trans.Suo J., Chen X., Shan S., Gao W., and Dai Q., “ A concatenational graph evolution aging model,” IEEE Trans.Suo J., Zhu S., Shan S., and Chen X., “ A compositional and dynamic model for face aging,” IEEE Trans.K., “ Age-invariant face recognition,” IEEE Trans. S., “ Age synthesis and estimation via faces: A survey,” IEEE Trans. Lanitis A., “ Comparative evaluation of automatic age-progression methodologies,” EURASIP J.Lanitis A., “ Evaluating the performance of face-aging algorithms,” in Proc.and Chellappa R., “ Modeling shape and textural variations in aging faces,” in Proc. Wang J., Shang Y., Su G., and Lin X., “ Age simulation for face recognition,” in Proc.
#Face morph age progression applications skin
M., and Thalmann D., “ A plastic-visco-elastic model for wrinkles in facial animation and skin aging,” in Proc. B., “ The perception of human growth,” Sci. Psychology: Human Perception Perform., vol. E., “ Aging faces as viscal-elastic events: Implications for a theory of nonrigid shape perception,” J. Heafner H., “ Age-progression technology and its application to law enforcement,” in Proc.Both visual and quantitative assessments show that the approach advances the state-of-the-art. Quantitative evaluations by a COTS face recognition system demonstrate that the target age distributions are accurately recovered, and 99.88 and 99.98 percent age progressed faces can be correctly verified at 0.001 percent FAR after age transformations of approximately 28 and 23 years elapsed time on the MORPH and CACD databases, respectively. The proposed method is applicable even in the presence of variations in pose, expression, makeup, etc., achieving remarkably vivid aging effects.
#Face morph age progression applications generator
Further, an adversarial learning scheme is introduced to simultaneously train a single generator and multiple parallel discriminators, resulting in smooth continuous face aging sequences. To render photo-realistic facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer way. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while keeping personalized properties stable. This paper presents a novel generative adversarial network based approach to address the issues in a coupled manner. The two underlying requirements of face age progression, i.e., aging accuracy and identity permanence, are not well studied in the literature.