Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation

• Published in: IEEE transactions on image processing Vol. 24; no. 12; pp. 5356 - 5368
• Main Authors: Jiwen Lu, Liong, Venice Erin, Jie Zhou
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Age determination; Aged; Aged, 80 and over; Aging - physiology; Anthropometry - methods; Artificial Intelligence; Binary codes; Binary system; biometrics; Codes; costsensitive learning; Estimation; Face; Face - anatomy & histology; Facial; Facial age estimation; Feature extraction; feature learning; Humans; Image Processing, Computer-Assisted - methods; Learning; Learning systems; Mathematical analysis; Mathematical models; Middle Aged; multi-feature learning; Pattern Recognition, Automated - methods; Pixels; Representations; Robustness; Training; Young Adult; biometrics; feature learning; Facial age estimation; multi-feature learning; cost-sensitive learning
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2015.2481327

In this paper, we propose a cost-sensitive local binary feature learning (CS-LBFL) method for facial age estimation. Unlike the conventional facial age estimation methods that employ hand-crafted descriptors or holistically learned descriptors for feature representation, our CS-LBFL method learns discriminative local features directly from raw pixels for face representation. Motivated by the fact that facial age estimation is a cost-sensitive computer vision problem and local binary features are more robust to illumination and expression variations than holistic features, we learn a series of hashing functions to project raw pixel values extracted from face patches into low-dimensional binary codes, where binary codes with similar chronological ages are projected as close as possible, and those with dissimilar chronological ages are projected as far as possible. Then, we pool and encode these local binary codes within each face image as a real-valued histogram feature for face representation. Moreover, we propose a cost-sensitive local binary multi-feature learning method to jointly learn multiple sets of hashing functions using face patches extracted from different scales to exploit complementary information. Our methods achieve competitive performance on four widely used face aging data sets.