Face Detection Using Linear Discriminant Analysis (Lda) Method and Support Vector Machine (Svm) - Fajar Hariadi; Riwa Rambu Hada Enda (JOINCS (Journal of Informatics, Network, and Computer Science), 2019)
Safety and comfort are basic needs that must be met by all humans. People use CCTV that is often used to monitor public areas that have many people. Initially images from CCTV cameras were only sent via cable to a certain monitor room and needed direct supervision by security personnel with still low image resolution. One method for identifying faces is the Linear Discriminant Analysis (LDA) method. LDA is a method to find a linear subspace that maximizes the separation of two classes of patterns according to Fisher Criterion (fisher criteria weight). This study aims to detect faces with the Linear Discriminant Analysis (LDA) method as extraction features and classify facial images using the Support Vector Machine (SVM) method. The conclusion of this study is that the results obtained from face detection get a fairly high percentage of 84.2% for detected faces and 15.8% for undetectable faces and the results obtained are influenced by a fairly good facial image and image cropping process good and unchanging face position which makes it easy to detect faces.