Face feature based extraction and classification for Gender Recognition and Age Estimation using Fuzzy Model

Abstract

Facial demographic classification is an attractive topic in computer vision. Attributes such as age and gender can be used in much real life application such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparison. Our proposed approach is based on Multi Level features which are extracted from normalized face images. We proposed fuzzy model base gender prediction and mining base age estimation techniques. An approach for gender prediction fuzzy model show that gender prediction performance can be boosted by up to a great extend. Our study is based on a dataset from natural data mining competition. We propose architecture for gender prediction, which consists of the “fuzzy learning model". The experimental results will show our proposed method significantly outperform baseline methods. A detailed analysis of features provides an entertaining insight into behavior variation on female and male users. Furthermore, the combination of visual cues resulted almost as strong as textual analysis in predicting gender, while providing complementary information that can be employed to further boost gender prediction accuracy will be up to 96%. As a byproduct of our investigation, we were also able to extrapolate the semantic categories of posted pictures mostly correlated to males and females. Keywords: Fuzzy Logic, Face Features Extraction (FFE), Data Mining, Gender prediction, Age Estimation, Visual analytics