Multi Task Algorithm for Smart Campus Face Recognition Based on Deep Learning Framework Caffe

With the rapid advancement of artificial intelligence (AI) technology, especially breakthroughs in pattern recognition, facial recognition technology has become one of the core topics in computer vision research. The introduction of deep learning (DL) technology has brought a revolutionary leap in the performance of facial recognition algorithms, significantly improving the accuracy and efficiency of recognition. In the wave of smart campus construction, facial recognition technology, with its unique advantages, has been widely used in key scenarios such as access control management, student attendance, library borrowing systems, etc. It not only greatly improves the daily management efficiency of schools, but also effectively enhances the security protection level of campuses. In response to the practical needs of smart campuses, this article innovatively proposes a multi task algorithm for smart campus face recognition based on the DL framework Caffe. This algorithm cleverly integrates the idea of multi task learning, aiming to achieve more accurate and efficient facial recognition functions by simultaneously optimizing multiple sub tasks such as face detection, feature extraction, and identity recognition. The experimental results show that this algorithm exhibits significant advantages in recognition speed, accuracy, and other aspects compared to traditional methods.