An Efficient Age Estimation System with Facial Makeover Images Based on Key Points Selection

Age is one of the essential factors in establishing the identity of the person. Estimation of the human age is a procedure adopted by anthropologists, archaeologists and forensic scientists. Compared with other cognition problems, age estimation from face images is still very challenging. Predicting and estimating the age from facial images with makeup is an interesting task in digital entertainment. Estimating age from a facial image is an intriguing and exigent task. Aging changes both shape as well as texture and it is an irreversible, uncontrollable and personalized. The efficiency of the age estimation system degrades with respect to facial makeover. The main objective of this research is to estimate the age of a human from the facial image with makeup. Initially, the face image will be normalized by employing a face detection algorithm. After detecting the face exactly, we have extracted the unique features (key points) from the images such as texture, shape and regions. Estimating the age of a person with different makeovers is not an easy task. To overcome this difficulty, we have to identify the uniqueness of each image of a same person. The eye part does not change whatever the person having the makeup. So the eyes are same for the person with different makeover. For region area or key points, the eye portion will be segmented from the detected face image. The shape feature can be extracted by Active Appearance Model (AAM). Finally, based on the feature library, the image can be classified under a particular age group using Artificial Neural Network (ANN). After the classification the age can be predicted. The proposed approach will be implemented in MATLAB and planned to be evaluated using various facial makeover images.