A New Two-Step Method for Intelligent Age Estimation

Intelligent age estimation via face images has become an important research topic in machine vision and pattern recognition fields because it has a key role in many applications such as customer behavior analysis in a business intelligence system. Age estimation is a process which analyses an individual face image and estimates his/her age based on the year measure. The age estimation process includes two parts: 1) face representation and 2) learning the age estimation function. So far several methods have been presented for improvement of one or both parts of the age estimation process. In this paper، a new method is presented for improvement of learning age estimation part. In the proposed method faces are represented by BIF (Bio-Inspired Features) and PLS (Partial Least Squares) methods، and then individual’s age is estimated by a new two-step method which is implemented based on MLP (Multi-Layer Perceptron) neural network. The proposed method is evaluated using two benchmark databases FG-NET and MORPH2. The results show that the proposed method improves the age estimation process (The proposed method gains 54% of improvement on FG-NET and also 78% of improvement on MORPH2).