Age estimation in facial images using histogram equalization
A. Deepa, T. Sasipraba · 2017
The digital world draws sensitive attention in the field of face recognition and facial age estimation. Despite of various transpiring researches in this domain, invigoration is indeed required. With respect to various challenges, the estimation of age requires consideration of several aspects. The estimation of age can be refined by considering both geometrical measures and texture analysis. The steps to be followed are normalization, face cropping, filtering, feature extraction and finally classification to provide the refined age of the face image. The normalization of face is done using histogram equalization to study the intensity component. From the image the face is cropped. The median filter is applied on the image to detach noise in the image and to maintain the edge details. The requisite features are extracted from the image such as geometric measures, texture details. The structure of texture features and the intensity transitions are analyzed. With these descriptors, the age of the image is classified.