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Human injected by Botox age estimation based on active shape models, speed up robust features, and support vector machine

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Abstract

Anti-aging and looking young with a full of vigor appearance with no Facial volume depletion and deepening lines of facial expression is a dream of every human being in life. Researchers in dermal and cosmetic fields had spent many years looking for solutions to aging signs and wrinkles other than surgeries. Botox is a skin rejuvenation cosmetic procedure that represents the recent magical key to aging appearance problems especially with the fascinating results it had showed. Botox can simply make you look 10 to 20 years younger, which represent an obstacle in the face of human age estimation researches. In this paper, we proposed a new model called Human Injected by Botox Age Estimation (HIBAE) model, a human age estimator based on active shape models, speed up robust feature, and support vector machine to accurately estimate the age of people that are exposed to Botox injections. Human Injected by Botox Age Estimation proposed model was trained by a crossover of Productive Aging Lab. database and 60 images collected from the internet of people that were exposed to Botox, and tested using a crossover of FACES64 database and 20 images of people that were exposed to Botox. HIBAE had showed superiority through performance testing over the state-of-the-art.

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Authors and Affiliations

  1. Department of Computer Science Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

    Shahenda Sarhan, Sabaa Hamad & Samir Elmougy

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  1. Shahenda Sarhan
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  2. Sabaa Hamad
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  3. Samir Elmougy
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Correspondence to Shahenda Sarhan.

Additional information

The article is published in the original.

Shahenda Sarhan is computer science assistant professor in Mansoura University since 2012. She had her Ph.D., M.Sc., and B.Sc. in computer sciences from Mansoura University in Egypt. Shahenda is interested in Artificial intelligence, Distributed Computing and image processing.

Samir Elmougy received the B.Sc in Statistics and Computer Science in 1993 and the M.Sc. in Computer Science in 1996, both from Mansoura University, Egypt. He received the Ph.D. in computer science from the School of Electrical Engineering and Computer Science, Oregon State University, USA, in 2005. He is working as the Chair of the Department of Computer Science, Faculty of Computers and Information, Mansoura University since Dec. 2014. From 2008 to 2014, he had been with King Saud University, Riyadh, Saudi Arabia as an assistant professor at the Dept. of Computer Science, College of Computers and Information Sciences. His current research interests include algorithms for error correcting codes, computer networks, machine learning, and software engineering.

Sabaa Hamed had his B.Sc. in computer science from Tikrit University in Iraq and his M.Sc. also in computer science from Mansoura University in Egypt. Sabaa is interested in image processing and machine learning.

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Sarhan, S., Hamad, S. & Elmougy, S. Human injected by Botox age estimation based on active shape models, speed up robust features, and support vector machine. Pattern Recognit. Image Anal. 26, 617–629 (2016). https://doi.org/10.1134/S1054661816030184

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