Abstract: Ant Colony Optimization (ACO) is a novel evolutionary algorithm derived from the foraging behavior of real ants of nature, which can find the shortest path between a food source and their nest. The main characteristics of ACO are robustness, positive feedback and distributed computation. And at the same time, Support Vector Machine (SVM), based on structure risk minimization principle, has the better performance and the better generalization ability. According to these, a gender classification using SVM and ACO is presented. Firstly, to reduce the dimensionality of the face images, the principal component coefficients of all images are calculated through Karhunen Loeve transform. Then, the eigenvectors are sorted in the descending order of eigenvalues. Secondly, ACO decides which eigenvectors will be used. After ACOs feature selection, the SVMs are trained and tested for gender classification. Deserving the best optimal features with highest accuracy rate, the next validation is continued until 10-fold cross-validations are completed. The experiments indicate that the proposed gender classification system based on ACO and SVM is more practical and efficient in comparison with others.