The trajectory of a person's career is significantly influenced by attendance. The conventional register-based attendance system is tedious, time-consuming, and generally uninteresting. These age-old methods, being laborious and time-intensive, warrant a more efficient alternative. In this paper, we introduce a Bimodal Attendance system implemented through biometrics. We delve into the examination of key physical characteristics of a human being, such as the face and fingerprints. User enrollment involves collecting essential user information, including facial and fingerprint data. A web camera is employed to capture live facial biometrics, while the Mantra Fingerprint sensor (MFS100) is utilized for the acquisition of the user’s fingerprint image. The collected facial images undergo preprocessing to reduce noise, and facial recognition is accomplished by detecting facial landmarks. Implementation of Convolutional Neural Network-based facial recognition is executed using the Dlib package. Additionally, we propose a methodology for fingerprint verification utilizing Scale Invariant Feature Transformation (SIFT). Distinctive SIFT feature points are extracted in scale space based on texture information around the feature points, facilitating effective matching. In this multimodal attendance system, real-time attendance marking is achieved by capturing facial images. The fingerprint image is subsequently captured and verified if the recognized face corresponds to a registered user. Attendance records are updated in the database, ensuring accuracy surpassing 70% identification.