Please use this identifier to cite or link to this item: https://doi.org/10.1109/TMM.2011.2167317
Title: Web image and video mining towards universal and robust age estimator
Authors: Ni, B.
Song, Z. 
Yan, S. 
Issue Date: Dec-2011
Source: Ni, B.,Song, Z.,Yan, S. (2011-12). Web image and video mining towards universal and robust age estimator. IEEE Transactions on Multimedia 13 (6) : 1217-1229. ScholarBank@NUS Repository. https://doi.org/10.1109/TMM.2011.2167317
Abstract: In this paper, we present an automatic web image and video mining framework with the ultimate goal of building a universal human age estimator based on facial information, which is applicable to all ethnic groups and various image qualities. On one hand, a large (391 k) yet noisy human aging image database is collected from Flickr and Google Image using a set of human age-related text queries. Multiple human face detectors based on distinctive techniques are adopted for noise-prune face detection. For each image, the detected faces with high detection confidences constitute a bag of face instances. We further remove the outliers via principal component analysis (PCA), which results in a condensed image database with about 175 k face instances. A robust multi-instance regressor learning algorithm is then developed to learn the kernel regression-based human age estimator in the presence of bag label noises. On the other hand, about 10 k video clips are downloaded from YouTube. We extract tracked face sequences from these video clips. Although their age labels are unknown, the tracked faces within a sequence are naturally with identical ages. This age-consistence constraint for face pairs is used as an extra regularizer to enhance the robustness of the age estimator. The derived human age estimator is extensively evaluated on three benchmark human aging databases, and without taking any images from these benchmark databases as training samples, comparable age estima tion accuracies with the state-of-the-art results are achieved. © 2006 IEEE.
Source Title: IEEE Transactions on Multimedia
URI: http://scholarbank.nus.edu.sg/handle/10635/57805
ISSN: 15209210
DOI: 10.1109/TMM.2011.2167317
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

26
checked on Dec 12, 2017

Page view(s)

38
checked on Dec 8, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.