Age estimation from face images: Human vs. machine performance

A deep analysis on age estimation

“…MAE computes the average age deviation error in absolute terms, MAE = M i=1 |â i − a i |/M, witĥ a i the estimated age of the i-th sample, a i its real age and M the total of samples. CS is defined as the percentage of images for which the error e is no higher than a given number of years l, as CS (l) = M e≤l /M (Chang et al (2011);Weng et al (2013); Han et al (2013)). Related publications typically supply either an eleven-point curve for age deviations [0 − 10], or simply the value CS (5).…”

“…With regards to the learning algorithm, several approaches have been proposed, including, among others, Support Vector Machines / Regressors (Guo et al (2009);Han et al (2013);Chang et al (2011);Weng et al (2013)), neural networks (Lanitis et al (2004)) and their variant of Conditional Probability Neural Network (Geng et al (2013)), Random Forests (Montillo and Ling (2009)), and projection techniques such as Partial Least Squares (PLS) and Canonical Correlation Analysis (CCA), along with their regularized and kernelized versions (Guo and Mu (2011). An extensive comparison of these classification schemes for age estimation has been reported ; Huerta et al (2014)), and the advantageousness of CCA was demonstrated over others, both regarding accuracy and efficiency.…”

“…For instance, BioInspired Features (BIF) (Riesenhuber and Poggio (1999)) and its derivations have consistently been used for age estimation in the last years (Geng et al (2013); Han et al (2013)). These feed-forward models intertwine a number of convolutional and pooling layers.…”

“…The most typical ones consist of either hand-tuned multi-level filter banks (Guo et al (2009) ;Geng et al (2013); Han et al (2013)), that intend to emulate the behavior of primary visual cortex cells, or fine-grained facial meshes to accomplish precise alignment through dozens of facial landmarks (Chang et al (2011);Geng et al (2007); Lanitis et al (2004)). In any case, the resulting extraction schemes are difficult to replicate, and the high-dimensional visual descriptors in many cases take considerable time to be computed.…”

Multi-way regression for age prediction exploiting speech and face image information

“…A comprehensive overview of research activities in facial age estimation can be found in [1]. Facial ageing patterns are extracted for automatic age estimation in [3], while a hierarchical approach is proposed in [4]. This approach initially performs age group classification and subsequently, Support Vector Regression (SVR) is applied to predict the final age.…”