Integrated segmentation and recognition of hand-printed numerals
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Age classification from facial images
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Method for Estimating and Modeling Age and Gender using Facial Image Processing
VSMM '01 Proceedings of the Seventh International Conference on Virtual Systems and Multimedia (VSMM'01)
Robust Real-Time Face Detection
International Journal of Computer Vision
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Finding visual concepts by web image mining
Proceedings of the 15th international conference on World Wide Web
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regression From Uncertain Labels and Its Applications to Soft Biometrics
IEEE Transactions on Information Forensics and Security
Human Age Estimation With Regression on Discriminative Aging Manifold
IEEE Transactions on Multimedia
Comparing different classifiers for automatic age estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
IEEE Transactions on Image Processing
Age classification for pose variant and occluded faces
Proceedings of the international conference on Multimedia
Learning local features for age estimation on real-life faces
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Learning facial attributes by crowdsourcing in social media
Proceedings of the 20th international conference companion on World wide web
Age regression from soft aligned face images using low computational resources
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Sense beauty via face, dressing, and/or voice
Proceedings of the 20th ACM international conference on Multimedia
Hi, magic closet, tell me what to wear!
Proceedings of the 20th ACM international conference on Multimedia
People search and activity mining in large-scale community-contributed photos
Proceedings of the 20th ACM international conference on Multimedia
Image and Vision Computing
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In this paper, we present an automatic web image mining system towards building a universal human age estimator based on facial information, which is applicable to all ethnic groups and various image qualities. First, a large (Flickr and Google image search engine based on a set of human age related text queries. Then, within each image, several human face detectors of different implementations are used for robust face detection, and all the detected faces with multiple responses are considered as the multiple instances of a bag (image). An outlier removal step with Principal Component Analysis further refines the image set to about 220k faces, and then a robust multi-instance regressor learning algorithm is proposed to learn the kernel-regression based human age estimator under the scenarios with possibly noisy bags. The proposed system has the following characteristics: 1) no manual human age labeling process is required, and the age information is automatically obtained from the age related queries, 2) the derived human age estimator is universal owing to the diversity and richness of Internet images and thus has good generalization capability, and 3) the age estimator learning process is robust to the noises existing in both Internet images and corresponding age labels. This automatically derived human age estimator is extensively evaluated on three popular benchmark human aging databases, and without taking any images from these benchmark databases as training samples, comparable age estimation accuracies with the state-of-the-art results are achieved.