Parametric Real Face Images Detection System (RFIDS) Using Multiple Classifiers

Recently biometric researches against spoofing attacks has been an important role of study, today we can examine the improvement of this biometric security technology against challenging methods such as spoofing attacks. In this thesis software-based approach is presented based on image quality assessments (IQA) to discriminate real genuine face images from impostor samples, a liveness assessment method is added to the present system to ensure friendly use, processing speed, and non-intrusive biometric system. The proposed method RFIDS uses 15 image quality features to decrease the level of complexity and make the system applicable for real-time applications. The experimental results achieved from this implemented work on an available dataset generates a high degree of positive detection compared to other existing methods and that the 15 image quality measures (parameters) are efficient in classifying real faces from printed impostor samples. There are some useful information retrieved from real images using IQA that makes the system capable enough to discriminate them from printed traits. Keywords: Image quality assessment, biometric, real and spoof face detection.