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

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.