In this work, we propose a novel fractal-based technique to analyze pseudo 2D representation of 1D retinal nerve fiber layer (RNFL) thickness measurement data vector set for early detection of glaucoma. In our proposed technique, we first convert the 1D RNFL data vector sets into pseudo 2D images and then exploit 2D fractal analysis (FA) technique to obtain the representative features. These 2D fractal-based features are further processed using principal component analysis (PCA) and the final classification between normal and glaucomatous eyes is obtained using Fischer's linear discriminant analysis (LDA). An independent dataset is used for training and testing the classifier. The technique is used on randomly selected GDx variable corneal compensator (VCC) eye data from 227 study participants (116 patients with glaucoma and 111 patients with healthy eyes). We compute sensitivity, specificity and area under receiver operating curve (AUROC) for statistical performance comparison with other known techniques. Our classification performance shows that fractal-based technique is superior to the standard machine classifier Nerve Fiber Indicator (NFI).