Glaucoma is an optic neuropathy, which leads to vision loss and is irreversible due to damage in the optic nerve head mainly caused by increased intra-ocular pressure. Retinal fundus photography facilitates ophthalmologist in detection of glaucoma but is subjective to human intervention and is time-consuming. Computational methods such as image processing and machine learning classifiers can aid in computer-based glaucoma detection which helps in mass screening of glaucoma. In this context, the proposed method develops an automated glaucoma detection system, in the following steps: (i) pre-processing by segmenting the blood vessels using directional filter; (ii) segmenting the region of interest by using statistical features; (iii) extracting the clinical and texture-based features; and (iv) developing ensemble of classifier models using dynamic selection techniques. The proposed method is evaluated on two publically available datasets and 300 fundus images collected from a hospital. The best results are obtained using ensemble of random forest using META-DES dynamic ensemble selection technique, and the average specificity, sensitivity and accuracy for glaucoma detection on hospital dataset are 100%, respectively. For RIM-ONE dataset, the average specificity, sensitivity and accuracy for glaucoma detection are 100%, 93.85% and 97.86%, respectively. For Drishti dataset, the average specificity, sensitivity and accuracy for glaucoma detection are 90%, 100% and 97%, respectively. The quantitative results and comparative study indicate the ability of the developed method, and thus, it can be deployed in mass screening and also as a second opinion in decision making by the ophthalmologist for glaucoma detection.