• Satish Kumar Nath

In recent years, face biometric security systems are rapidly increasing. Face-spoofing attacks, in which a spoofed face is presented to the biometric system in an attempt to be authenticated, are becoming an inevitable threat. Therefore, face-spoofing detection has become a critical requirement for any face recognition system to filter out fake faces. While face anti-spoofing techniques have received much attention to aim at identifying whether the captured face is genuine or fake, most face-spoofing detection techniques are biased towards a specific presentation attack type or presentation device; failing to robustly detects various spoofing scenarios. To mitigate this problem, we aim at developing a generalizable face-spoofing framework which able to accurately identify various spoofing attacks and devices. This innovative technology shows a lot of promise and change the way in which we can access sensitive information. But as promising as facial recognition is it does have flaws. User photos can easily be found on social networking sites and images can be spoofed. This is where the need of anti-spoofing comes into play. Face anti spoofing is the task of preventing false facial verification by using a photo, video/substitute for an authorized person’s face.

- Anti-Spoofing, Deep Learning, Spoof Detection, Biometric Security, Facial Recognition, Liveness Detection, Presentation Attack, Convolutional Neural Networks (CNN), Feature Extraction, Multimodal Fusion, Imposter Detection, Supervised Learning, Unsupervised Learning

"Survey of FACE ANTI-SPOOFING DETECTION MODEL THROUGH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d614-d618, May-2024, Available :http://www.jetir.org/papers/JETIR2405368.pdf

"Survey of FACE ANTI-SPOOFING DETECTION MODEL THROUGH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd614-d618, May-2024, Available at : http://www.jetir.org/papers/JETIR2405368.pdf