Web Image Mining Towards Universal Age Estimator


Bingbing Ni , Zheng Song , Shuicheng Yan
National University of
Singapore
4 Engineering Drive 3
Singapore 117576

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ABSTRACT
In this paper, we present an automatic web image mining system
towards building a universal human age estimator based on facial
information, which is applicable to all ethnic groups and various
image qualities. First, a large (∼391k) yet noisy human aging image
dataset is crawled from the photo sharing website Flickr and
Google image search engine based on a set of human age related
text queries. Then, within each image, several human face detectors
of different implementations are used for robust face detection, and
all the detected faces with multiple responses are considered as the
multiple instances of a bag (image). An outlier removal step with
Principal Component Analysis further refines the image set to about
220k faces, and then a robust multi-instance regressor learning algorithm
is proposed to learn the kernel-regression based human
age estimator under the scenarios with possibly noisy bags. The
proposed system has the following characteristics: 1) no manual
human age labeling process is required, and the age information is
automatically obtained from the age related queries, 2) the derived
human age estimator is universal owing to the diversity and richness
of Internet images and thus has good generalization capability,
and 3) the age estimator learning process is robust to the noises
existing in both Internet images and corresponding age labels. This
automatically derived human age estimator is extensively evaluated
on three popular benchmark human aging databases, and without
taking any images from these benchmark databases as training samples,
comparable age estimation accuracies with the state-of-the-art
results are achieved


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