Comparative Analysis of Image Processing-Based Age Estimation Algorithms

The human body grows and develops with age. Age-Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. One of the main reasons is that the aging effects on human faces present several unique characteristics that make age estimation a challenging task that requires non-standard classification approaches. Age estimation has become a challenging problem due to uncontrollable environment, insufficient and incomplete training data, strong person-specific, and large age span variation. In real life, computer vision applications requiring automatic age estimation from face images have attracted increasing attention. In this paper, we conferred the main perspectives used to improve the performance of the age estimation system, present the various types of techniques used in age estimation, and reveal where the experiments are being carried out. In addition to that several aging databases consisting of age descriptions are described briefly. Eventually, we presented a comparative analysis between all the preferred methods according to the techniques used.