The ability to classify age from a facial image has not been pursued in computer vision. This research addresses the limited task of age classification of a facial image into a baby, young adult, and senior adult. This is the first reported work to classify age, and to successfully extract and use natural wrinkles. We present a theory and practical computations for visual age classification from facial images, based on cranio-facial changes in feature-position ratios, and on skin wrinkle analysis. Three age groups are classified.>

In the problem area of human facial image processing, the first computational task that needs to be solved is that of detecting a face under arbitrary scene conditions. Although some progress towards this has been reported in the literature, face detection remains a difficult problem. In this paper the authors report on a novel face-finding method that appears quite robust. First, "snakelets" are used to find candidate edges. Candidate ovals (face-locations) are then found from these snakelets using a voting method. For each of these candidate face-locations, the authors use a method introduced previously to find detailed facial features. If a substantial number of the facial features are found successfully, and their positions satisfy ratio-tests for being standard, the procedure positively reports the existence of a face at this location in the image.

This paper presents a theory and practical computations for the recognition of a facial image given a police-artist sketch. First, the facial features of the police sketch are located automatically. Then, regions centered at these positions that contain high intensity variations are temporarily removed. Then a standardization step transforms the sketch image into a "photograph-like" image. After this transformation, the high intensity variation regions that were removed are placed back into the standardized image. The resulting image is then passed to the recognition part of our algorithm. In this stage, principle component analysis is performed on both the standardized image to be recognized and the facial images of the training set. Results using actual police sketches and arrest photographs are presented.>