A Semi-Supervised Approach to Perceived Age Prediction from Face Images

We address the problem of perceived age estimation from face images, and propose a new semi-supervised approach involving two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster-representative samples for covering as many clusters as possible. This simple sampling strategy allows us to boost the performance of a manifold-based semi-supervised learning method only with a relatively small number of labeled samples. The second contribution is to take the heterogeneous characteristics of human age perception into account. It is rare to misjudge the age of a 5-year-old child as 15 years old, but the age of a 35-year-old person is often misjudged as 45 years old. Thus, magnitude of the error is different depending on subjects’ age. We carried out a large-scale questionnaire survey for quantifying hu...

• Type: Journal
• Year: 2010
• Where: IEICET
• Authors: Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara
• Keywords: Age Estimation, Human Age Perception, IEICET 2010, Semi-supervised Learning