The process of age determination could figure in a variety of applications ranging from access control, human machine interaction, person identification and data mining and organization. In some cases the age estimation problem is treated individually (Kwon 1999, Guo 2008, Fu 2008, Wang 2009) whereas in other cases age estimation and age progression are both treated using similar methodologies (Lanitis 2002, Geng 2007, Suo 2008). However, the two problems are in effect inverse problems since in age estimation information extracted from face images is used for determining the age of a subject whereas in age progression given a target age a face image that displays typical aging characteristics associated with the target age group is synthesized. Both age estimation and age progression need to take into account age-related facial deformations encountered during the lifetime of a subject. Age progression is the prediction of the future facial appearance of a subject based on images showing his/her previous facial appearance. The facial age estimation problem shares similarities with the age progression problem. In the case that age-group classification is considered, the percentage of correct age-group classifications can also be used for performance evaluation. The CS measure is regarded as a more representative measure in relation with the performance of an age estimator. (Geng 2007) also propose the use of the cumulative score (CS) that shows the percentage of cases among the test set where the age estimation error is less than a threshold. The most widely used error metric is the Mean Absolute Error (MAE) between actual and estimated ages of faces in a test set. This parameter is an important aspect of the problem as different aging characteristics appear in different age groups hence a system trained to deal with a specific age range may not be applicable to more diverse age ranges.Īn important aspect of the age estimation problem is the formulation of suitable metrics for assessing the performance of age estimators. Another important factor pertaining to the age estimation problem is the range of ages considered. Among the three variations listed above age-group classification is the most widely used as in most applications it is only necessary to obtain a rough estimate of a subject’s age rather than his/her exact age. According to the application for which an age estimation system is intended to be used, the output of the classification stage can be an estimate of the exact age of a person or the age group of a person or even a binary result indicating whether the age of a subject is within a certain age range. The facial age estimation problem shares several similarities with other typical face image interpretation tasks where the execution stage includes the process of face detection, location of facial characteristics, feature vector formulation and classification. In automatic facial age estimation the aim is to use dedicated algorithms that enable the estimation of a person’s age based on features derived from his/her face image. However, researchers who carried out work in studying the process of age estimation by humans (Rhodes 2009) conclude that humans are not so accurate in age estimation hence the possibility of developing automatic facial age estimation methods poses an attractive direction.įigure 1: Example of aging effects for a subject. The observation of aging-related features on faces allows humans to estimate the age of other persons just by looking at their face. Usually bone growth takes place during childhood whereas during adult ages the most intense age-related deformations are linked with texture changes. Facial aging effects are mainly attributed to bone movement and growth and skin related deformations associated with the introduction of wrinkles and reduction of muscle strength (Albert 2007, Rhodes 2009). The appearance of a human face is affected considerably by aging (see Figure 1).
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