Abstract book (pdf) - ICPR 2010

siderable variations in camera settings, facial poses, and illumination conditions. In this paper, we apply a recently-proposed
machine learning technique called covariate shift adaptation to alleviating lighting condition change between laboratory
and practical environment. Through real-world age estimation experiments, we demonstrate the usefulness of our proposed
method.
10:00-10:20, Paper ThAT6.4
Ranking Model for Facial Age Estimation
Yang, Peng, Rutgers Univ.
Lin, Zhong, Rutgers Univ.
Metaxas, Dimitris, Rutgers Univ.
Feature design and feature selection are two key problems in facial image based age perception. In this paper, we proposed
to using ranking model to do feature selection on the haar-like features. In order to build the pairwise samples for the ranking
model, age sequences are organized by personal aging pattern within each subject. The pairwise samples are extracted
from the sequence of each subject. Therefore, the order information is intuitively contained in the pairwise data. Ranking
model is used to select the discriminative features based on the pairwise data. The combination of the ranking model and
personal aging pattern are powerful to select the discriminative features for age estimation. Based on the selected features,
different kinds of regression models are used to build prediction models. The experiment results show the performance of
our method is comparable to the state-of-art works.
10:20-10:40, Paper ThAT6.5
Development of Recognition Engine for Baby Faces
Di, Wen, Tsinghua Univ.
Zhang, Tong, Hewlett-Packard Lab.
Fang, Chi, Tsinghua Univ.
Ding, Xiaoqing, Tsinghua Univ.
Existing face recognition approaches are mostly developed based on adult faces which may not work well in distinguishing
faces of kids. Especially, baby faces tend to have common features such as round cheeks and chins, so that current face
recognition engines often fail to differentiate them. In this paper, we present methods for discriminating baby faces from
adult faces, and for training a special engine to recognize faces of different babies. To achieve these, we collected a huge
number of baby face images and developed a software system to annotate the image database. Experimental results prove
that the trained baby face recognizer achieves dramatic improvement on differentiating baby faces and the fusion of it
with the conventional adult face recognition engine also works well on the overall data set containing both baby and adult
faces.
ThAT7 Dolmabahçe Hall C
Document Retrieval Regular Session
Session chair: Faruquie, Tanveer (IBM Res. India)
09:00-09:20, Paper ThAT7.1
An Information Extraction Model for Unconstrained Handwritten Documents
Thomas, Simon, LITIS
Chatelain, Clement, LITIS Lab. INSA de Rouen
Heutte, Laurent, Univ. de Rouen
Paquet, Thierry, Univ. of Rouen
In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents
is introduced. Unlike classical approaches found in the literature as keyword spotting or full document recognition, our
approch relies on a strong and powerful global handwriting model. A entire text line is considered as an indivisible entity
and is modeled with Hidden Markov Models. In this way, text line shallow parsing allows fast extraction of the relevant
information in any document while rejecting at the same time irrelevant information. First results are promising and show
the interest of the approach.
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