Nokta Aglari ile 3B Seyrek Y z Modellerinde Canlilik Tespiti

Nowadays, face-based authentication systems play a critical role in high-security areas. These systems are widely preferred due to their contactless and fast usage advantages, significantly improving security processes. However, digital fraud techniques such as printed photos, screen images, and video-based spoofing threaten the reliability of face recognition systems. In this study, a real-time and high-accuracy face liveness detection method is proposed to detect face spoofing performed through passive methods. The proposed system analyzes the three-dimensional structure of the face using a stereo imaging technique and classifies real and fake faces through a deep learning model trained on a specially created dataset. This passive face liveness analysis method, which does not require any active user interaction, provides an effective solution against spoofing attempts, enhancing the reliability of face recognition-based authentication systems in high-security areas. Experimental studies have shown that the proposed method achieves a high accuracy rate against photo and screen-based attacks.