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The identification of gender in human-computer interaction is currently a serious issue (HCI). Gender classification determines a person's gender, such as male or female, using biometric cues. In order to learn a gender recognizer, characteristics from facial images are often retrieved and then exposed to a classifier. Gender classification is used in a wide variety of applications, such as passive monitoring, control in smart buildings (restricting access to specific regions based on gender), supermarkets, gender advertising, and security investigation. In this study, gender is categorised using the face distance measurement as a progenitor. We explored a wide range of machine learning methods, such as logistic regression, k nearest neighbour, SVM, random forest, decision trees, neural networks and got the best SVM accuracy of 97.5.