Comparing different classifiers for automatic age estimation

By Andreas Lanitis, C. Draganova, C. Christodoulou. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004

636 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/af961f130fa89225848c96c28df60972059d398a

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In Computer Vision and Pattern Recognition

Citations in the corpus

Human age estimation using bio-inspired features (2009, background): “The age estimation can be viewed as a multi-class classification problem [20] [30] [14], a regression problem [8] [35] [34] [7] [38], or a hybrid of the two [16] [14] [15].”
Age-Invariant Face Recognition (2010, neutral): “There has also been some work done in the related area of age estimation using statistical models, e.g., [4], [14].”
Facial Age Estimation by Learning from Label Distributions (2010, methodology): “One of the early works on exact age estimation was done by Lanitis et al. [20], [19], where the aging pattern was represented by a quadratic function called aging function .”
Improving Person Re-identification by Attribute and Identity Learning (2017, methodology): “We first describe two baselines in Section 4.1 and then the APR network in Section 4.2”
Cumulative Attribute Space for Age and Crowd Density Estimation (2013, background): “human age and people count), and its estimation can be obtained by solving a multi-class classification problem [13, 21].”
Understanding images of groups of people (2009, background): “In the past, human accuracy for the age recognition task has been measured [12, 17], although the effect of context from other people in images on human performance has (a) (b) (c)”
Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “Due to that geometry and texture features are helpful to distinguish baby, adult, and senior, many methods were proposed based on the AAM model [8] since it is a natural tool to simultaneously model the shape and texture of facial images [19][12].”
Age Synthesis and Estimation via Faces: A Survey (2010, methodology): “In terms of utilizing the AAMs for encoding face images, Geng et al. [131], [37] used 200 AAMs features to encode each face image, while only 50 features were used in [17].”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, neutral): “(2004) [138] AAM Private 40 MAE=3.”
Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011, methodology): “In our work, the absolute cost associated with the MAE serves as the cost sensitivity function: costkplq |l k|, for k 1, ...,K. (5) The CS performance measurement proposed by Geng et al. [10] is defined as CSpLq pMe L{Mq 100%, (6) where Me L is the number of test images with the absolute error e les”
Human Age Estimation With Regression on Discriminative Aging Manifold (2008, methodology): “These overall results are consistent with the reports from [11], [12].”
Modeling Age Progression in Young Faces (2006, background): “Further, Lanitis et al. [12] compared the above age estimation process with that of neural networks based approaches.”
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].”