Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression

By Guo-qing Guo, Yun Fu, Charles R. Dyer, Thomas S. Huang. IEEE Transactions on Image Processing, 2008

727 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/281e1000e0f9873f217a1d5895036ee72a54bf7a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Age and Gender Estimation of Unfiltered Faces (2014, methodology): “The UIUC-IFP-Y Internal Aging [13], [14], extensively used by the SMILE lab at Northeastern University, is not publicly available due to intellectual property limitations, but presumably may be obtained by contacting its authors”
Cumulative Attribute Space for Age and Crowd Density Estimation (2013, methodology): “Settings – For FG-NET, we followed the same leave-oneperson-out setting as in [7, 15, 32, 33, 35].”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “[145] explored learning manifolds based on projections that preserve orthogonal locality.”
AgeDB: The First Manually Collected, In-the-Wild Age Database (2017, methodology): “IFP-Y [9, 15] 2008 8,000 1600 Accurate to the year Yes No”
Facial Age Estimation by Learning from Label Distributions (2010, background): “Index Terms—Age estimation, face image, label distribution, machine learning Ç”
Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011, methodology): “[11] introduced an age manifold scheme and combined SVR and SVM to learn and predict human ages.”
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, neutral): “2007), age manifold (Fu and Huang 2008; Guo et al. 2008; Guo and Mu 2011), biologically-inspired models [including biologically-inspired features (BIF) (Guo et al.”
Neighborhood repulsed metric learning for kinship verification (2012, background): “Representative examples including face recogni tion [2, 15, 21, 22, 29], facial expression recognition [6], age estimation [7, 9, 14], gender classification [17] and eth nicity recognition [10].”
Age Synthesis and Estimation via Faces: A Survey (2010, methodology): “Guo et al. [31], [16] chose the SVM as a representative classifier and the SVR as a representative regressor, and compared their performance using the same input data.”
Multi-task warped Gaussian process for personalized age estimation (2010, methodology): “Moreover, in [5, 11], the authors 1 assumed that the images form an aging manifold and used some manifold learning methods such as conformal embedding analysis to discover the manifold structure in the data.”
Age and gender classification using convolutional neural networks (2015, methodology): “On a different line of work are methods that represent the aging process as a subspace [16] or a manifold [19].”
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.”
Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval (2014, background): “Most existing age-related works for face image analysis focus on age estimation [14,15,40,25,34,7,38,6,10,22,21] and age simulation [16,30,28,29,24].”
Human age estimation using bio-inspired features (2009, background): “Different people have different rates of aging process [15] [14], which is determined by not only the people's gene but also many factors, such as health condition, living style, working environment, and sociality [29] [5].”