earticle

영어

With the evolution of HCI (Human-Computer Interaction), the computer vision systems are playing an important role in our lives. Some of the prime areas of computer vision applications include gender detection, face recognition, body tracking and ethnicity identification etc. Automated data analyses techniques help discover regularities and hidden associations in larger volumes of datasets. Classification being a data mining technique is largely used to group categorical data as well as a blend of continuous numeric values and categorical data. A number of classification techniques like decision trees, support vector machine (SVM), nearest neighbors and neural networks etc. have gained popularity in numerous areas of data mining practices. Among these classification techniques, decision trees offer an added advantage of producing easily interpretable rules and logic statements along with generating the classification tree for the given dataset. This study offers a distinct method for gender classification of facial images. We have used a variant of the decision tree algorithm for gender classification of frontal images due to its distinctive features. Our technique demonstrates robustness and relative scale invariance for gender classification. Details of the experimental design and the results are reported herein.