Face Recognition Using Fisherface and Support Vector Machine Method
Syachrul Qolbi Nur Septi, Intan Nurma Yulita, Herlina Napitupulu · 2021
COVID-19 is declared as a pandemic by WHO and until now COVID-19 pandemic remains a problem in 2021. Many efforts have been made to reduce the spreading virus, one way to reduce its spread is by wearing a mask but most people often ignore it. Monitoring large groups of people becomes difficult by the government or the authorities. Face recognition, a biometric technology, is based on the identification of a face features of a person. This paper describes a face recognition using Fisherface and Support Vector Machine method to classify face mask dataset. Face recognition using Fisherface method is based on Principal Component Analysis (PCA) and Fisher's Linear Discriminant (FLD) method or also known as Linear Discriminant Analysis (LDA). The algorithm used in the process for feature extraction is Fisherface algorithm while classification using Support Vector Machine method. The results show that for face recognition on face mask dataset using cross validation with 10 fold, the average percentage accuracy is 99.76%.