Gender identification has its unique importance in sports and forensic sciences. However, the social issues are major constraints to identify the gender of a human being. A novel approach for gender classification using facial EMG is presented. The time domain features of facial EMGs are explored towards their robustness in gender identification. A Davies-Bouldin index is calculated to evaluate the features. Anterior belly of digastrics along with Orbicularis Oris Inferior showed the promising classification accuracy of 80% with the reported features. A possible enhancement in accuracy could be possible by a larger dataset.