On Gait Attributes: Age, Gender, and Attractiveness by Video-based Gait Analysis
In this talk, we will describe a series of our work on gait attributes via video-based gait analysis. First, we will give a brief introduction to age estimation using a standard gait features such as gait energy image (GEI) and frequency-domain feature as well as a standard regression method, i.e., Gaussian process regression. We will then introduce our recent, the world largest gait databases for age estimation which were collected in conjunction with long-term exhibition of intuitive video-based gait analysis demonstration in a science museum. Thereafter, we introduce two recent approaches to gait-based age estimation. One is a hierarchical approach, where a gait sample is projected into a low-dimensional embedding space, then classified into one of age groups by a directed acyclic graph support vector machine, and finally fed into an age group-dependent support vector regressor to estimate its age. The other one is based on a state-of-the-art deep learning architecture, i.e., DenseNet. In addition to the above-mentioned gait-based age estimation, we also construct an age progression/regression model based on mean GEI of each age group and free-form deformation between the mean GEIs of adjacent age groups, which has potential applications to gait recognition robust against time elapse and physical gait age estimation. Finally, we will briefly address gait attractiveness estimation based on human perception.
Yasushi Makihara, received the B.S., M.S., and Ph.D. degrees in Engineering from Osaka University in 2001, 2002, and 2005, respectively.
He is currently an Associate Professor of the Institute of Scientific and Industrial Research, Osaka University. His research interests are computer vision, pattern recognition, and image processing including gait recognition, pedestrian detection, morphing, and temporal super resolution. He is a member of IPSJ, IEICE, RSJ, and JSME. He has obtained several honors and awards, including the 2nd Int. Workshop on Biometrics and Forensics (IWBF 2014), IAPR Best Paper Award, the 9th IAPR Int. Conf. on Biometrics (ICB 2016), Honorable Mention Paper Award, the 28th British Machine Vision Conf. (BMVC 2017), Outstanding Reviewers, the 11th IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG 2015), Outstanding Reviewers, and the 30th IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2017), Outstanding Reviewers. He has served as an associate editor of IPSJ Transactions on Computer Vision and Applications (CVA), a program co-chair of the 4th Asian Conf. on Pattern Recognition (ACPR 2017), and reviewers of journals such as T-PAMI, T-IP, T-CSVT, T-IFS, IJCV, Pattern Recognition, and international conferences such as CVPR, ICCV, ECCV, ACCV, ICPR, FG, etc.