African Computer Vision

African computer vision research aims to address the underrepresentation of African data and perspectives within the field, focusing on developing inclusive models and datasets. Current efforts concentrate on creating large-scale datasets representing diverse African populations, particularly for tasks like facial recognition, iris biometrics, and named entity recognition in African languages, often employing techniques like k-means clustering and transfer learning. This work is crucial for mitigating biases in existing algorithms and enabling the development of computer vision applications relevant and beneficial to African communities, thereby fostering broader participation in the field.