Abstract: To discriminate between real genuine face and impostor printed face sample has been an important field in biometric authentication researches, recently researches were done on this particular field to improve protection on biometric systems. In this paper 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 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 are efficient in classifying real faces from printed impostor samples. There are some useful informationís retrieved from real images using IQA that makes the system capable enough to discriminate them from printed traits, the implemented approach uses 15 classification methods to ensure the efficiency of our introduced work.
Keywords: Genuine face, biometric authentication, IQA, non-intrusive biometric, quality features.