Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion

Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 20; no. 7; pp. 2049 - 2062
Main Authors:	Jian-Gang Wang, Sung, E, Wei-Yun Yau
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.07.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Active learning Adolescent Adult Age age estimation Age Factors Algorithms Applied sciences Artificial Intelligence Biometric Identification - methods Child Child, Preschool Classification Classification algorithms Cluster Analysis Databases, Factual Discriminant Analysis Exact sciences and technology Face Face - anatomy & histology Facial furthest nearest neighbor Humans Image processing incomplete data problem Infant Information, signal and communications theory Labeling Learning Marking Middle Aged Pattern recognition Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Studies Support vector machines Telecommunications and information theory Training Training data Biometrics Performance evaluation Automatic classification Age estimation Image processing Labelling Iterative method Support vector machine Learning Database Selection criterion Margin-based method incomplete data problem Learning algorithm Categorization Private life High performance Nearest neighbour Discriminant analysis Face recognition Active learning Two dimensional model Pattern recognition furthest nearest neighbor Signal classification Kernel method Palm On line processing Automatic recognition Palm print
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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