A STRUCTURE BASED HUMAN FACIAL AGE ESTIMATION FRAMEWORK UNDER A CONSTRAINED CONDITION

Face recognition is the process of identifying one or more people in images or videos by analyzing and comparing patterns. The system proposes a new approach to sense feature extraction for age face recognition. This approach consists of two steps. First, an encoding scheme is devised that compresses high-dimensional dense features into a compact representation by maximizing the intruder correlation. Second, feature is extracted from the detected face. Our objective in this thesis is to develop a human age estimator from face images. Given a face image of the person, we label it with an estimated age. We propose a four-stage fusion framework for facial age estimation. This framework starts from gender recognition, and then to the second phase, genderspecific age grouping, and followed by the third stage, age estimation within age groups, and finally ends at the fusion stage for the output, we will have the extracted image and the estimated age of the person.