Search

This paper reveals various constraints in digital way of estimating the age of human facial image. The age estimation involves analyzing the facial features and classifying the age in accordance to the extracted feature values. The biometric features such as the facial parts (eye, mouth, chin, forehead, hair), texture, color and shape provide essential aging details. In the proposed method, the fractal features are extracted using fractal directional code method to retain the significant details present in the input image. The fractal features along with the local features are used to train the system. The deep neural network with three layers is used to train the system. The scaled conjugate gradient back propagation trains the system and the age is classified into seven age groups (0-10, 11-19, 20-29, 30-39, 40-49, 50-59 and 60-69). Higher accuracy is achieved by concocting the biometric features and the fractal features. [ABSTRACT FROM AUTHOR]