Automatic Age Estimation Based on LBP and GLCM Features Using SVM

Abstract

Face is generally considered as the reference frame of mind. Therefore, to estimate the feeling of the mind, many authors have considered the emotions from the facial expressions into consideration to identify the state of mind of an individual. Hence in this article we proposed a methodology for automatic age estimation based on Local Binary Pattern (LBP) and Grey Level Co- Occurrence Matrix (GLCM). The facial features are extracted using LBP and GLCM and these features are given as input’s to the Support Vector Machine (SVM) for age estimation. The experimentation on proposed method is carried out using FG-NET database and Mean Absolute Error (MAE) is calculated to compare the proposed method with state-of-the-art algorithms. Finally, the proposed methodology demonstrates the classification accuracy above 88%.

Hybridized Feature Extraction for Facial Age Estimation using SVM

“…Three specific aging datasets were used to estimate age and experimental results were recorded to be robust and effective [19]. Rampay and Satyanarayana [21] proposed a methodology for automatic age estimation based on Local Binary Pattern (LBP) and Grey Level Co-Occurrence Matrix (GLCM). Using LBP and GLCM, the local facial features are extracted and these characteristics are given as inputs for age calculation to the SVM.…”