The nature of statistical learning theory
The nature of statistical learning theory
Age classification from facial images
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Cascaded Classification of Gender and Facial Expression using Active Appearance Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
IEEE Transactions on Image Processing
Learning local features for age estimation on real-life faces
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
A comparative study of active appearance model annotation schemes for the face
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Ensemble of global and local features for face age estimation
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Gender classification via global-local features fusion
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Sensitivity analysis with cross-validation for feature selection and manifold learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Age estimation from digital pictures of the face is a very promising research field that is now receiving wide attention. As with any good research problem, face age-estimation is wrought with many challenging interactions that cannot easily be separated out. In general, aging patterns are well understood for all humans, however, these patterns become confounded by intrinsic factors of genetics, gender differences, and ethnic deviations and, equally as important, extrinsic factors of the environment and behavior choices (i.e. sun exposure, drugs, cigarettes, etc). This novel work focuses on the development of a generalized multi-ethnic age-estimation technique--the first of its kind. In addition to the novelty of this approach, the system's overall performance measure (MAE) is "on par" with algorithms that are tuned for a specific ethnic group. Further, the proposed system performance proves to be far more stable across age than the best published results.