Design and Development of Student Attendance Application Using Viola-Jones Methodand Haar Cascade Algorithm at UIM - Gunawan, Lukman Syahrul; Umam, Busro Akramul; Makruf, Masdukil (Universitas Panca Marga, 2023)
The recognition of human facial images is one of the key technologies that continues to be developed. In the field of Computer Vision, it finds its applications in biometric recognition systems, search and indexing in digital video databases, restricted area access control systems, video conferences, human-computer interaction, and more. The Viola-Jones method is an object detection method known for its high accuracy of approximately 93.7%, and it is 15 times faster than the Rowley Baluja-Kanade detector and around 600 times faster than the Schneiderman-Kanade detector. The Haar Cascade Classifier algorithm is one of the algorithms used to detect a face. The Cascade Classifier is utilized in attendance recording with real-time face recognition, enabling the real-time recording of students.To enhance the abstract, it could be mentioned that to further improve the effectiveness and accuracy of face recognition systems, researchershave explored combining the Viola-Jones method with other techniques. For instance, the Haar Cascade Classifier algorithm can be combined with the Local Binary Pattern (LBP) algorithm, which preserves important information in images and performs well under low lighting conditions. By integrating these techniques, face recognition systems can achieve better results in classifying human heads, which are often the primary objects of interest in face recognition. Additionally, in conducting photo capture experiments using a camera, it is important to employ grayscale coloration, which retains the grayscale information in the images. Furthermore, capturing six photos during the process ensures more effective results and facilitates the assignment of unique IDs for easier data differentiation.