BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Learning from facial aging patterns for automatic age estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adjusted Robust Regression for Human Age Estimation
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Face Age Classification on Consumer Images with Gabor Feature and Fuzzy LDA Method
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Age Synthesis and Estimation via Faces: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal hyperplanes ranker with cost sensitivities for age estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Comparing different classifiers for automatic age estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning universal multi-view age estimator using video context
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Generally, age estimation is formulated as a single-label based problem. However, since aging is a gradual process and people are always in transition period between ages, labeling a facial example with an exact age is a difficult problem. Meanwhile, sufficient training data is lack for many ages. In this paper, to improve the accuracy of age estimation, we propose a novel approach by applying Multi-Label Learning to the age features. In the proposed approach, each facial image is treated as an example associated with the origin label as well as its neighboring ages, which makes the data more reliable and sufficient. The motivation comes from the observation that, with age changes slowly and smoothly, people would look quite like themselves before and after several years. Experiments show that the proposed approach outperforms the traditional age estimation approaches.