Locally Adjusted Robust Regression for Human Age Estimation

Guo, Guodong; Fu, Yun; Huang, Thomas S.; Dyer, Charles R.

doi:10.1109/wacv.2008.4544009

Cited by 108 publications

(97 citation statements)

References 19 publications

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“…The worst set had MAE of 7.86 years, this is primarily because the test set contained outlier ages. Considering automatic age estimation systems reported in the literature, [34] proposed a locally adjustable robust regressor (LARR) that outperformed other state of the art systems. The MAE of our best test set is about 41% ((5.07 -2.99)/5.07) lower than Guo et al's [34] best result.…”

Section: Methodsmentioning

confidence: 99%

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Individualised model of facial age synthesis based on constrained regression

Bukar

Ugail

Connah

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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-Faces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets, unrestrained environments, ethnic and gender variations to mention but a few. This work presents a new approach to automatic facial ageing which involves the development of a person specific facial ageing system. A color based Active Appearance Model (AAM) is used to extract facial features. Then, regression is used to model an age estimator. Age synthesis is achieved by computing a solution that minimises the distance from the original face with the use of constrained regression. The model is tested on a challenging database of single image per person. Initial results suggest that plausible images can be rerendered at different ages, automatically using the AAM representation. Using the constrained regressor we are guaranteed to get estimated ages that are exact for an individual at a given age.

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Section: Methodsmentioning

confidence: 99%

“…age ≈ f (c) (12) Regression methods have been used in the literature to relate the AAM parameters to a person's age [5], [29], [30]. There are many types of regression analysis depending on the distribution of the response variable.…”

Section: Ageing Functionmentioning

confidence: 99%

Individualised model of facial age synthesis based on constrained regression

Bukar

Ugail

Connah

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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“…The aging trend is learned in a low dimensional domain using manifold embedding techniques. The mapping from the image space to the low dimensional manifold space can be done either by linear or by nonlinear functions [16][17][18][19][20][21] …”

Section: Age Estimation Methodsmentioning

confidence: 99%

Facial Age Estimation based on Decision Level Fusion of AAM, LBP and Gabor Features

Günay¹,

Nabiyev²

2015

ijacsa

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Abstract-In this paper a new hierarchical age estimation method based on decision level fusion of global and local features is proposed. The shape and appearance information of human faces which are extracted with active appearance models (AAM) are used as global facial features. The local facial features are the wrinkle features extracted with Gabor filters and skin features extracted with local binary patterns (LBP). Then feature classification is performed using a hierarchical classifier which is the combination of an age group classification and detailed age estimation. In the age group classification phase, three distinct support vector machines (SVM) classifiers are trained using each feature vector. Then decision level fusion is performed to combine the results of these classifiers. The detailed age of the classified image is then estimated in that age group, using the aging functions modeled with global and local features, separately. Aging functions are modeled with multiple linear regressions. To make a final decision, the results of these aging functions are also fused in decision level. Experimental results on the FG-NET and PAL aging databases have shown that the age estimation accuracy of the proposed method is better than the previous methods.

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“…Age manifold utilizes a manifold embedding technique to discover the aging trend in a low dimensional domain from many face images at each age. Thus, the mapping from the image space to the manifold space can be done either by linear or by nonlinear functions [24][25][26][27][28], such as Y=P(X, L), where X is the image space sampled by a set of face images, , -. A ground truth set , -associated with images provides the age labeling.…”

Section: Introductionmentioning

confidence: 99%