Human Age Estimation Based on Locality and Ordinal Information

• Published in: IEEE transactions on cybernetics Vol. 45; no. 11; pp. 2522 - 2534
• Main Authors: Li, Changsheng, Liu, Qingshan, Dong, Weishan, Zhu, Xiaobin, Liu, Jing, Lu, Hanqing
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Age determination; Age estimation; Aged; Aging; Aging - physiology; Algorithms; Artificial Intelligence; Child; Child, Preschool; Correlation; Datasets; Discriminant analysis; Energy measurement; Estimation; Face; Face - physiology; Facial; feature selection; Humans; Image Processing, Computer-Assisted - methods; Infant; Infant, Newborn; Linear programming; local manifold structure; Manifolds; Middle Aged; Optimization; ordinal pattern; Preserves; semi-supervised learning; Young Adult; ordinal pattern; Age estimation; semi-supervised learning; feature selection; local manifold structure
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2014.2376517

In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original high-dimensional feature space. Thus, we first measure the energy of each feature in preserving the underlying local structure information and the ordinal information of the facial images, respectively, and then we intend to learn a low-dimensional aging representation that can maximally preserve both kinds of information. To further improve the performance, we try to eliminate the redundant local information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation among features. Finally, we formulate all these issues into a unified optimization problem, which is similar to linear discriminant analysis in format. Since it is expensive to collect the labeled facial aging images in practice, we extend the proposed supervised method to a semi-supervised learning mode including the semi-supervised feature selection method and the semi-supervised age prediction algorithm. Extensive experiments are conducted on the FACES dataset, the Images of Groups dataset, and the FG-NET aging dataset to show the power of the proposed algorithms, compared to the state-of-the-arts.