Age estimation from human body images

In this paper, we investigate the problem of estimating human ages from full body images. To our best knowledge, this problem has not been formally addressed before possibly due to the great challenges and lacking of such publicly available datasets. However, estimating human ages at a distance has a number of potential applications, especially for visual surveillance in such places as supermarkets, airports, building entrances, and shopping malls. In this paper, we propose a new human age estimation approach from full body images with frontal or back views. Our contributions are three-fold. First, we collect a human body image dataset containing 1500 public figures or celebrities searched from the internet, as well as the age label information of each image. Second, we explore several widely used human local appearance feature descriptors with a regression model to estimate human ages from these body images. Lastly, we apply a multiview canonical correlation analysis (MCCA) method by making use of multiple feature descriptors to exploit complementary information to further improve the age estimation performance. Experimental results have clearly demonstrated the feasibility of using fully body images to estimate human age and the efficacy of our proposed approach.