Authors:
Krešimir Bešenić
1
;
Igor Pandžić
1
and
Jörgen Ahlberg
2
Affiliations:
1
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
;
2
Computer Vision Laboratory, Linköping University, 58183 Linköping, Sweden
Keyword(s):
Age, Video, Benchmark, Semi-Supervised, Pseudo-Labeling.
Abstract:
Taking a better look at subjects of interest helps humans to improve confidence in their age estimation. Unlike still images, sequences offer spatio-temporal dynamic information that contains many cues related to age progression. A review of previous work on video-based age estimation indicates that this is an underexplored field of research. This may be caused by a lack of well-defined and publicly accessible video benchmark protocol, as well as the absence of video-oriented training data. To address the former issue, we propose a carefully designed video age estimation benchmark protocol and make it publicly available. To address the latter issue, we design a video-specific age estimation method that leverages pseudo-labeling and semi-supervised learning. Our results show that the proposed method outperforms image-based baselines on both offline and online benchmark protocols, while the online estimation stability is improved by more than 50%.