As humans, we are easily able to categorize a person’s age group from an image of the person’s face but humans are also far from perfect in this task. And this ability has not largely been pursued in the computer vision community. Up to date, the proven algorithms only differentiate the image’s age group; further that we have only claims of different researchers.

Any progress in the research community’s understanding of the remarkable ability that human’s have with regard to facial image analysis will go a long way toward the broader goals of face-recognition and facial-expression recognition. In the long run, besides leading to a theory for automatic precise age identification which would assist robots in numerous ways, analysis of facial features such as aging-wrinkles will assist in wrinkle analysis for facial-expression recognition. [1].

An improvement of our understanding of how humans may classify age from visual images can be used in various areas, such as:

• indexing into a face database by the person’s age,
• in the area of newspaper-story understanding,
• in the application areas such as gathering population age-statistics visually (for example, getting the ages of patrons at entertainment and amusement parks or in television network viewer-rating studies.)

The Big Picture

First of all, this is not the complete picture of "age related studies in pattern recognition".

For example there's a huge field of "effects of human aging in handwriting".

This picture is concerned mainly on facial image related studies in pattern recognition.

Two Different Approaches

Methods Applied so Far

This figure shows a short illustration on methods applied for age estimation. Of course, this is not the whole picture, but represents the overwhelming majority.

References

[1] Kwon, Loboy, "Age Classification from Facial Images", Computer Vision and Image Understanding, 1999, Vol. 74, No. 1, April, pp. 1–21.
[2] Ramanathan, Chellappa, Biswas, "Age progression in Human Faces : A Survey"