AGE GROUP ESTIMATION USING MACHINE LEARNING TECHNIQUES: A REVIEW

There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security screening and human interaction. machine. However, despite advances in automatic age estimation, this remains a difficult problem. Indeed, the aging process of the face is determined not only by intrinsic factors, e.g. genetic factors, but also by extrinsic factors, e.g. lifestyle, expression and environment. As a result, different people with the same age may have very different appearances because of different rates of facial aging Real-time audience measurement system includes five continuous steps: face detection, face tracking, gender identification, age rating and cloud data data analysis. The challenge of such a system is based on part-time algorithm machine learning methods. The face of the face is determined by various factors: genealogy, lifestyle, expression and environment. That's why people at the same age may have a very different rate of growth. We recommend a novel algorithm that consists of two stages: Local binary patterns and support vector machine classification based adaptive feature. Experimental results on the FG-NET, MORPH and our own database are presented. Estimation of human capability is studied by the ability to use crowd sourcing that allows the ability to combat the capabilities of machines and humans