Ensuring the authenticity of student identity during examinations remains a significant challenge in higher education, where conventional attendance procedures based on manual verification and examination slips are vulnerable to impersonation, administrative inefficiencies, and human error. This study presents an intelligent deep learning-based face recognition system developed to automate examination attendance management within the Faculty of Computing, Northwest University, Kano. The proposed system integrates Multi-task Cascaded Convolutional Networks (MTCNN) for robust face detection and alignment with FaceNet for discriminative facial feature embedding and identity recognition. To demonstrate its practical applicability, a web-based supervisor interface was developed to support real-time student authentication, automated attendance recording, secure storage of attendance records, and flexible attendance retrieval through student-based and course-based CSV reports. The proposed system utilized a facial image dataset collected from 69 registered students across four departments within the Faculty of Computing, Northwest University, Kano. Enrolment images were used to generate facial embeddings for each registered student, while the system was evaluated using 3,450 test images to assess its face recognition performance and practical applicability for examination attendance management. Experimental evaluation showed that the system correctly recognized 3,441 facial images, yielding an overall recognition accuracy of 99.74%, while achieving an average precision, recall, and F1-score of 0.99. Functional evaluation further confirmed the system's capability to perform reliable real-time attendance recording, prevent duplicate attendance entries, and efficiently generate attendance reports required for examination administration. Although recognition performance was influenced by variations in illumination and image quality, the overall findings demonstrate that the developed system provides an accurate, efficient, and practical biometric solution for strengthening examination attendance management and mitigating impersonation within the Faculty of Computing, Northwest University, Kano.