Abstract book (pdf) - ICPR 2010

10:20-10:40, Paper ThAT5.5
On Selecting an Optimal Number of Clusters for Color Image Segmentation
Le Capitaine, Hoel, Univ. of La Rochelle
Frelicot, Carl, Univ. of La Rochelle
This paper addresses the problem of region-based color image segmentation using a fuzzy clustering algorithm, e.g. a
spatial version of fuzzy c-means, in order to partition the image into clusters corresponding to homogeneous regions. We
propose to determine the optimal number of clusters, and so the number of regions, by using a new cluster validity index
computed on fuzzy partitions. Experimental results and comparison with other existing methods show the validity and the
efficiency of the proposed method.
ThAT6 Topkapı Hall B
Face Ageing Regular Session
Session chair: Yanikoglu, Berrin (Sabanci Univ.)
09:00-09:20, Paper ThAT6.1
Cross-Age Face Recognition on a Very Large Database: The Performance versus Age Intervals and Improvement
using Soft Biometric Traits
Guo, Guodong, West Virginia Univ.
Mu, Guowang, North Carolina Central Univ.
Ricanek, Karl, Univ. of North Carolina
Facial aging can degrade the face recognition performance dramatically. Traditional face recognition studies focus on
dealing with pose, illumination, and expression (PIE) changes. Considering a large span of age difference, the influence
of facial aging could be very significant compared to the PIE variations. How big the aging influence could be? What is
the relation between recognition accuracy and age intervals? Can soft biometrics be used to improve the face recognition
performance under age variations? In this paper we address all these issues. First, we investigate the face recognition performance
degradation with respect to age intervals between the probe and gallery images on a very large database which
contains about 55,000 face images of more than 13,000 individuals. Second, we study if soft biometric traits, e.g., race,
gender, height, and weight, could be used to improve the cross-age face recognition accuracies, and how useful each of
them could be.
09:20-09:40, Paper ThAT6.2
A Ranking Approach for Human Age Estimation based on Face Images
Chang, Kuang-Yu, Acad. Sinica
Chen, Chu-Song, Acad. Sinica
Hung, Yi-Ping, National Taiwan Univ.
In our daily life, it is much easier to distinguish which person is elder between two persons than how old a person is. When
inferring a person’s age, we may compare his or her face with many people whose ages are known, resulting in a series of
comparative results, and then we conjecture the age based on the comparisons. This process involves numerous pairwise
preferences information obtained by a series of queries, where each query compares the target person’s face to those faces
in a database. In this paper, we propose a ranking-based framework consisting of a set of binary queries. Each query
collects a binary-classification-based comparison result. All the query results are then fused to predict the age. Experimental
results show that our approach performs better than traditional multi-class-based and regression-based approaches for age
estimation.
09:40-10:00, Paper ThAT6.3
Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation
Ueki, Kazuya, NEC Soft, Ltd.
Sugiyama, Masashi, Tokyo Inst. of Tech.
Ihara, Yasuyuki, NEC Soft, Ltd.
Over the recent years, a great deal of effort has been made to age estimation from face images. It has been reported that
age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions.
However, it is not straightforward to achieve the same accuracy level in real-world environment because of con-
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