Age-related macular degeneration (AMD) is a leading cause of vision loss. The presence of drusen are often associated to AMD. Drusen are tiny yellowish-white extracellular buildup present around the macular region of the retina. Clinically, ophthalmologists examine the area around the macula to determine the presence and severity of drusen. However, manual identification and recognition of drusen is subjective, time consuming and expensive. To reduce manual workload and facilitate large-scale early AMD screening, it is essential to detect drusen automatically. In this paper, we propose to use biologically inspired features (BIF) for the purpose of AMD detection. The optic disc and macula are detected to determine a focal region around macula for feature extraction. The extracted features are then classified using support vector machines (SVM). Our experimental results, tested on 350 images, demonstrate that the biologically inspired features from the focal region is effective for drusen detection with a sensitivity of 86.3% and specificity of 91.9%. The results of our proposed approach can be used to reduce workload of ophthalmologists and diagnosis cost.