Klasifikasi Gender Berdasarkan Sidik Jari Menggunakan Principal Component Analysis dan Support Vector Machine

Abstract

Most fingerprint classification research uses features such as core and delta as its basis. Before extracting fingerprint features, various preprocessing steps are usually performed first. This study differs from other studies in that classification is performed directly on the fingerprint image without going through a detailed preprocessing step and only a pixel size change is performed to 96x103. Fingerprint features are not determined manually, but are extracted automatically using the Principal Component Analysis (PCA) method which produces the 4200 best features. For feature perfection reasons, feature normalization has been performed using StandardScaler. The classification of this study uses a nonlinear Support Vector Machine (SVM) method with a Polynomial kernel. This study uses 6000 data samples from the SOCOFing database. This model obtains a classification accuracy of up to 88.75%.