Adaptive image optimization for difficult lighting conditions in face recognition

With the rapid development of the era of artificial intelligence, our lives have become more comfortable, and "face scanning" using facial recognition technology has become a new way of life. Facial recognition is a biometric technology that uses devices such as cameras to take photos containing faces, recognize faces in photos, and obtain information about facial features to match. Facial recognition technology belongs to a broad category of biometric technologies used by government and private institutions to identify people. The system includes the collection and recognition of facial images, extraction of key points, image processing, extraction of facial features, and comparison of facial recognition results. This article describes the advanced multiband Retinex algorithm, which allows processing images with uneven lighting and is integrated into the Yolov5 object detection pipeline. To evaluate this method, a dataset was collected from photographs of 3,045 students in various lighting scenarios with controlled changes in illumination achieved using a software light source. This method preserves image details and increases contrast, resulting in better detection accuracy while maintaining computational efficiency. The experimental results showed that the proposed approach can be more effective than traditional methods of obtaining images of faces in uneven lighting conditions.