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@article{199195,
        author = {GANDLA VIVEK VARDHAN and Dr. D BABU RAO and AKSHAYA NIMISHAKAVI and HARSHAVARDHAN REDDY CHARLAPALLY and HARSHITHA LAKKAKULA},
        title = {FACIAL RECOGNITION ATTENDANCE SYSTEM INTEGRATING OPENCV AND DEEP LEARNING MODELS FOR REAL-TIME VERIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11871-11880},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199195},
        abstract = {Attendance management is an essential process in educational institutions and organizations to monitor participation and maintain accurate records. Traditional attendance systems such as manual registers and card-based methods are often time-consuming, error-prone, and susceptible to proxy attendance. To address these challenges, this project proposes a Face Recognition Based Attendance Management System that automates the attendance process using computer vision and machine learning techniques. The system captures real-time video through a webcam and detects human faces using the Multi-task Cascaded Convolutional Neural Network (MTCNN) algorithm. The detected faces are then processed using a trained machine learning classifier to identify individuals by comparing them with a pre-trained dataset. Once recognition is completed, the system automatically records attendance along with details such as name, date, time, and confidence score, and stores the information in a structured database for report generation. The system is developed using Python and supporting libraries such as OpenCV for image processing, NumPy for numerical operations, Pandas for data management, and Scikit-learn for model training and classification. The proposed system reduces manual effort, improves accuracy, prevents fraudulent attendance practices, and provides a contactless and efficient attendance solution. The experimental results demonstrate that the system performs reliable real-time recognition under controlled conditions and can be further enhanced by integrating deep learning models, cloud databases, and mobile applications for scalability and improved performance.},
        keywords = {Face Recognition, Attendance Management System, Computer Vision, Machine Learning, MTCNN, OpenCV, Support Vector Machine (SVM), Real-Time Processing, Biometric Authentication, Automation.},
        month = {April},
        }