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Automated classification of age from appearance information is largely underexplored. In [2], Kwon et al. made an attempt to classify images of people into three age categories: children, adults and senior adults. This work was based on the cardioidal strain transformation to model the growth of a person s head from infancy to adulthood, obtained from cranio-facial research [1]. The revised cardioidal strain transformation describing head growth can be visualized as a series of ever-growing circles all attached at a common tangent base point, in this case the top of the head. According to this transformation, with an increase in age, the growth of lower parts of the face is more pronounced than that of the upper part. Thus, for example, within the top and bottom margins of the head, the eyes occupy a higher position in an adult than in an infant due to an outgrowing and dropping of the chin and jaw. This result was used in distinguishing between adults and babies. In order to do this the authors used facial feature detectors to detect the eyes, nose and the mouth. These feature positions were then used to find out certain ratios for each face and if the ratio was greater than a particular value, the face was classified as belonging to an adult, else it was classified as belonging to a baby. In order to distinguish between young adults and seniors the authors used snakes to determine the presence of wrinkles on the forehead of the person. The main problem with this method is the need to detect facial features, which is extremely error-prone. The costs and processing time of this approach limit its practicality for use in real applications. In [1,4] O Toole et al. used three-dimensional information for building a parametric 3D face model. They used caricature algorithms to exaggerate or de-emphasize the 3D facial features. In the resulting images the perceived age is changed based on whether the feature is exaggerated or de-emphasized. This result suggested that in older faces, the 3D facial features are emphasized. These results were verified by a number of observers. In a related work, Lanitis et al. [5] proposed a method for simulating aging in facial images, thus allowing them to age-normalize faces before using them for face recognition . For this purpose the authors used statistical model parameters that contain shape and intensity information. Some of these parameters were then used for simulating aging effects. Empirical results reported in this paper suggest that the face recognition accuracy improved when age normalization was carried out. These results indicate that the appearance of the face contains sufficient information regarding the age of the person that in turn could be used for classification purposes. In [2] it was shown that wrinkles could be used for differentiating between senior citizens and adults below a certain age. From this result it could be argued that the presence and absence of wrinkles, as indicated by the skin texture, could be detected even at low resolutions. Also, in [6] it has been stated that after age 45, the skin begins to thin, partially because of hormonal changes, resulting in a loss of volume and smoothness. In [3] it was found that it is easy to notice that there is a very big change in the facial skin texture of the person from age 40 to age 50. Keeping these points in mind, the two age categories used for classification were the age groups above 50 and below 40, keeping the ten years in between as a buffer zone where the performance of the classifier is unpredictable.
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