In this work, we investigate biometrics applied on 2D faces in order to secure areas requiring high security level. Based on emerging deep learning methods (more precisely transfer learning) as well as two classical machine learning techniques (Support Vector Machines and Random Forest), different approaches have been used to perform person authentication. Preprocessing filtering steps of input images have been included before features extraction and selection. The goal has been to compare those in terms of processing time, storage size and authentication accuracy according to the number of input images (for the learning task) and preprocessing tasks. We focus on data-related aspects to store biometric information on a low storage capacity remote card (10Ko), not only in a high security context but also in terms of privacy control. The proposed solutions guarantee users the control of their own biometrics data. The study highlights the impact of preprocessing to perform real-time computation, preserving a relevant accuracy while reducing the amount of biometric data. Considering application constraints, this study concludes with a discussion dealing with the tradeoff of the available resources against the required performances to determine the most appropriate method.