This paper proposes an advanced age-estimation approach that combines global and local features derived from a facial image. Active Appearance Models (AAMs) technique is used to construct the global facial features, while local facial features are generated from Local Binary Pattern (LBP) encoding. Ageestimation is performed in a two-step method: coarse (initial) prediction followed by a refini...

In this paper the age group estimation is presented based on combination of texture and fractal dimension features. The age of the human is used as one of the important key parameter for computer vision applications. The fractal dimension of the face image and the texture analysis is used to classify the age of the person into the three different groups such as child(10-20), young(21-50) and ol...

Perceptions of age influence how we evaluate, approach, and interact with other people. Based on a paramorphic human judgment model, the present study investigates possible determinants of accuracy and bias in age estimation across the adult life span. For this purpose, 154 young, middle-aged, and older participants of both genders estimated the age of 171 faces of young, middle-aged, and older...

The demand of estimation of age from facial images has tremendous applications in real world scenario like law enforcement, security control, and human computer interaction etc. However despite advances in automatic age estimation, the computer based age classification has become prevalent. The present paper evaluates the method of age group classification based on the Correlation Fractal Dimen...

Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optica...

The most prominent challenge in the facial age estimation is a lack of sufficient and incomplete training data. Aging is slower and gradual process, therefore, faces near close ages look quite similar this can allow us to utilize the face images at neighbouring ages with modeling to a particular age. There are many potential applications in age-specific human-computer interaction for security c...