Glaucoma detection from fundus images often relies on biomarkers such as the Cup-to-Disc Ratio (CDR) and Rim-to-Disc Ratio (RDR). However, precise segmentation of the optic cup and disc is challenging due to low-contrast boundaries and the interference of blood vessels and optic nerves. This article presents a novel automated framework for glaucoma detection that focuses on the rim structure as a biomarker, excluding the conventional reliance on CDR and RDR. The proposed framework introduces a Teacher-Student Generative Adversarial Network (TS-GAN) for precise segmentation of the optic cup and disc, along with a SqueezeNet for glaucoma detection. The Teacher model uses an attention-based CNN encoder-decoder, while the Student model incorporates Expectation Maximization to enhance segmentation performance. By combining implicit generative modeling and explicit probability density modeling, the TS-GAN effectively addresses the mode collapse problem seen in existing GANs. A rim generator processes the segmented cup and disc to produce the rim, which serves as input to SqueezeNet for glaucoma classification. The framework has been extensively tested on diverse fundus image datasets, including a private dataset, demonstrating superior segmentation and detection accuracy compared to state-of-the-art models. Results show its effectiveness in early glaucoma detection, offering higher accuracy and reliability. This innovative framework provides a robust tool for ophthalmologists, enabling efficient glaucoma management and reducing the risk of vision loss.