Abstract Human face is one of the most important biometrics as it contains information such as gender, race, and age. Identifying the gender based on human face images is a challenging problem that has been extensively studied due to its various relevant applications. Several approaches were used to address this problem by specifying suitable features. In this study, we present an extension of feature extraction technique based on statistical aggregation and Gabor filters. We extract statistical features from the image of a face after applying Gabor filters; subsequently, we use seven classifiers to investigate the performance of the selected features. Experiments show that the accuracy achieved using the proposed features is comparable to accuracies reported in recent studies. We used seven classifiers to investigate the performance of our proposed features. Experiments reveal that k-Nearest Neighbors algorithm (k-NN), K-Star classifier (K*), and Rotation Forest offer the best accuracies.

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