1. A method comprising: identifying a set of training vectors, wherein each training vector is mapped to either a male gender or a female gender, and wherein each training vector represents facial landmarks derived from a respective facial image; identifying an input vector of facial landmarks, wherein the facial landmarks of the input vector are derived from a particular facial image; selecting, from the input vector, a feature vector containing a subset of the facial landmarks, wherein selecting the feature vector comprises determining a training matrix representing the training vectors, determining a covariance matrix of the training matrix, and using a random forest technique to (i) build a plurality of trees, wherein each node of each tree in the plurality of trees represents a random selection of the facial landmarks, (ii) calculating the Gini importance of the facial landmarks, and (iii) based on the calculated Gini importance, determining the feature vector; performing, by a computing device, a weighted comparison between the feature vector and each of the training vectors; and based on a result of the weighted comparison, classifying the particular facial image as either the male gender or the female gender.