Gender classification using facial components.

Abstract

Gender classification is very important in facial analysis as it can be used as input into a number of systems such as face recognition. Humans are able to classify gender with great accuracy however passing this ability to machines is a complex task because of many variables such as lighting to mention a few. For the purpose of this research we have approached gender classification as a binary problem, involving the two classes male and female. Two datasets are used in this research which are the FG-NET dataset and Pilots Parliament datasets. Two appearance based feature extractors are used which are the LBP and LDP with the Active Shape model being included by fusing. The classifiers used here are the Support Vector Machine with Radial Basis Function kernel and an Artificial Neural Network with backpropagation. On the FG-NET an average detection of 90.6% against that of 87.5% to that of the PPB. Gender is then detected from the facial components the nose, eyes among others. The forehead recorded the highest accuracy with 92%, followed by the nose with 90%, cheeks with 89.2% and the eyes with 87% and the mouth recorded the lowest accuracy of 75%. As a result feature fusion is then carried out to improve classification accuracies especially that of the mouth and eyes with lowest accuracies. The eyes with an accuracy of 87% is fused with the forehead with 92% and the resulting accuracy is an increase to 93%. The mouth, with the lowest accuracy of 75% is fused with the nose which has an accuracy of 90% and the resulting accuracy is 87%. These results carried out by fusing through addition showed improved results. Fusion is then carried out between Appearance based and shape based features. On the FG-NET dataset using the LBP and LDP an accuracy of 85.33% and 89.53% with the PPB recording 83.13%, 89.3% for LBP and LDP respectively. As expected and shown by previous researchers the LDP clearly obtains higher classification accuracies as it than LBP as it uses gradient rather than pixel intensity. We then fuse the vectors of the LDP, LBP with that of the ASM and carry out dimensionality reduction, then fusion by addition. On the PPB dataset fusion of LDP and ASM records 81.56%, and 94.53% with the FG-NET recording 89.53% respectively.