• Dr. K.Niranjan Reddy
• D.Saketh
• G.Vinay
• G.Vaishnavi

Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or “spoofed”) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via backpropagation. We consider nine biometric spoofing benchmarks each one containing real and fake samples of a given biometric modality and attack type and learn deep representations for each benchmark by combining and contrasting the two learning approaches.

"Deep representations for Face and Fingerprint Spoofing Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a554-a559, November-2023, Available :http://www.jetir.org/papers/JETIR2311070.pdf

"Deep representations for Face and Fingerprint Spoofing Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa554-a559, November-2023, Available at : http://www.jetir.org/papers/JETIR2311070.pdf