Face-Based Attendance Data Using Principal Component Analysis - Aulia, Muhammad Arief; Furqan, Mhd.; Sriani, S (IJISTECH (International Journal of Information System and Technology), 2024)
The face is one of the easiest physiological measurements and is often used to distinguish the identity of one individual from another. This facial recognition process uses raw information from image pixels produced through the camera which is then represented in the Principal Components Analysis method. The way the Principal Components Analysis method works is by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector the eigenface value of each image will be obtained and then the closest eigenface value of the image will be searched for. image of the face you want to recognize. The test results show the overall success rate of face recognition that the application can carry out face recognition using digital camera hardware for the attendance system by displaying the name of the face owner as well as the date and time of recognition. The average accuracy value of the test with the light intensity level is 96.66%, the accuracy value The average test value with changes in the distance between the camera and the face was 98.33% and the average accuracy value of the test using glasses and hat accessories was 85%.