A component of the Cooperative Autonomous Mobile Robotic Systems(CAMoRoS) project is related to modeling and recognizing human stress and human affective states by computers. Several challenges need to be addressed including: video recognition, characterization of the behavioral stresses, and parameter extraction. To characterize the behavioral stresses, facial changes need to be detected. Our first step in the process of facial changes recognition is related toward the age estimation. We developed a new method, called locally adjusted robust regression (LARR) for age estimation by analyzing facial changes.
Human face, as a window to the soul, conveys important perceptible information related to individual traits. Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. People have the ability, developed early in life, to determine age between 20 and 60 years and conceive aging appearance from the face with high accuracy. Can a machine do the same job?
Derived from rapid advances in computer vision and pattern recognition, computer-based age estimation via face has become a particularly interesting topic recently because of the emergent real-world applications, such as electronic customer relationship management (ECRM), security control and surveillance monitoring, biometrics, and entertainment. Age estimation by machine has revealed as a difficult and challenging problem. Different people have different rates of aging, which is determined by not only the gene but also many factors, such as health condition, living style, working environment, and sociality. Aging shows different forms for different ages. From infancy to puberty, the greatest change is the craniofacial growth (shape change). Overall the face size gets larger gradually during the craniofacial growth. From adulthood to old age, the most perceptible change becomes the skin aging (texture change). The shape change still continues, but less dramatically. Thus, face aging is uncontrollable and personalized. Furthermore, males and females may have different face aging patterns displayed in images due to the different extent in using makeups and accessories. Many female face images may potentially show younger appearances.
To approach the problem we developed a new method, called locally adjusted robust regression (LARR) for age estimation. The description of the method is in the finding section of the report.