Age Group Estimation by Combining Texture and Fractal Analysis

Abstract

In this paper the age group estimation is presented based on combination of texture and fractal dimension features. The age of the human is used as one of the important key parameter for computer vision applications. The fractal dimension of the face image and the texture analysis is used to classify the age of the person into the three different groups such as child(10-20), young(21-50) and old(51 and above. The proposed approach of combing the fractal and texture features shows an effective estimation of the age group. The facial age groups are estimated with 90% average accuracy.

Automatic Age Estimation Based on LBP and GLCM Features Using SVM

“…Therefore, it is a challenging task to derive methodologies that can behold the data so that the private information totally secured. With this back drop, many Mathematical Innovations based on Cryptographic Models have evolved [1], [2], [3]. However, these models can be effective in case of texture transactions where the text can be encrypted using different variable length keys and can be decrypted at the other end using the inverse process.…”