The project task consists of a binary classification problem. The goal is to perform fingerprint spoofing detection, i.e. to identify genuine vs counterfeit fingerprint images. The dataset consists of labeled samples corresponding to the genuine (True, label 1) class and the fake (False, label 0) class. The samples are computed by a feature extractor that summarizes high-level characteristics of a fingerprint image. The data is 6-dimensional. The training files for the project are stored in file data/trainData.txt. The format of the file is a csv file where each row represents a sample. The first 6 values of each row are the features, whereas the last value of each row represents the class (1 or 0). The samples are not ordered. File data/evalData.txt contains more samples for the evaluation part. Various techniques were employed, including Gaussian models, logistic regression, Support Vector Machines (SVMs), and Gaussian Mixture Models (GMMs)