Learning from facial aging patterns for automatic age estimation

By Xin Geng, Zhi-Hua Zhou, Yu Zhang, Gang Li, Honghua Dai. ACM Multimedia, 2006

312 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6714ae96c5e811189a6b4c1e3783f44e5136763f

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In Computer Vision and Pattern Recognition

Citations in the corpus

Human age estimation using bio-inspired features (2009, methodology): “26 4 – – 8.06 AGES [12] – – 6.77 QM [20] , the " MAX " pooling is used to obtain the complex C layer features from the simple S layers.”
Deep convolutional neural networks-based age and gender classification with facial images (2017, methodology): “Zhou [7] used boosing as regression and Geng [8] proposed method of Aging Pattern Subspace.”
Facial Age Estimation by Learning from Label Distributions (2010, methodology): “Later, Geng et al. [12], [11] proposed the AGES algorithm based on the subspace trained on a data structure called aging pattern vector .”
Understanding images of groups of people (2009, methodology): “Our gender and age classifiers were motivated by the works of [12, 13] where a low dimension manifold for the age data.”
Multi-task warped Gaussian process for personalized age estimation (2010, methodology): “In [10, 9], a personalized age estimator was proposed by modeling the problem as a time series problem.”
Learning ordinal discriminative features for age estimation (2012, methodology): “The performance of age estimation is measured by the mean absolute error (MAE) and the cumulative score (CS) [12].”
Human Age Estimation With Regression on Discriminative Aging Manifold (2008, methodology): “To handle highly incomplete data due to the difficulty in data collection, AGing pattErn Subspace (AGES) [14], [15] models a sequence of personal aging face images by learning a subspace.”
Automatic Age Estimation Based on Facial Aging Patterns (2007, methodology): “This paper proposes an automatic age estimation method named AGES, which improves our earlier work [5].”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, methodology): “This approach was tested [147], [148] on the FG-NET face database, resulting in a reported MAE of 6.”