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Age Group Estimation using Facial Features

Human face is the non-verbal portal into the person. A person's gender, mood, country of origin, age category, and identity can be identified from the face. There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security control, and human computer interaction (HCI). This paper presents a method to improve the accuracy of the estimated age. Apart from geometric shape features, wrinkle analysis is also incorporated in classifying the age. Multiple algorithms are applied for different phases like feature extraction, illumination correction, image fitting and edge detection etc. The main objective of this paper is to present a working model of an age classifier that is more efficient that the existing models. The experimental results show that 93.01% recognition rate can be achieved when applying the proposed system on the images. The research in the field of image processing has undergone a rapid growth with the ability to solve the real world problems. Those researches include applications in tracking down a vehicle in an image, facial recognition, authentication purposes such as debit/credit cards, passports, voter's identification cards etc. Among these there are several applications that use the image of the user as the input and perform various operations on them. This paper presents an overview of the prior works in age estimation (determination) and a novel approach based on a hierarchical model, which infuses a classification system with multiple age estimator functions to create an industry age-estimation algorithm. It is a well-known fact that the biodynamic factors of facial aging are quite different for the two stages of aging: growth and development and adult. During the latter, the major changes in facial complex are due to lengthening and widening factors of cranial complex. The aging factor for adults does include some cranial changes, but the primary drivers are the development of wrinkles, lines, creases, and sagging of the skin. There are various methods used in this paper for age group estimation techniques that are currently deployed in areas like features extraction, age classification and texture analysis and the suitable algorithms are selected that efficiently suits the current needs.