Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Abstract

Malicious parties which impersonate systems by fake identities affect recognition performance of biometric systems. This study focuses on a strength anti-spoofing scheme based on decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involves consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this context, convolutional neural network (CNN) and Log-Gabor filter methods are used to learn deep representations and extract facial features of images respectively. In order to improve the robustness of proposed anti-spoofing framework, fusion of Log-Gabor and CNN methods is considered by applying decision-level-fusion technique. Finally, the performance of proposed antispoofing scheme is examined on public spoof databases such as Print-Attack and Replay-Attack face databases to detect fake facial images.

Unmasking the Imposters: Task-specific feature learning for face presentation attack detection

“…To address the longer response times in parallel fusion methods /citedwards2021effectiveness, a serial fusion method was applied using Siamese neural networks. Sharifi [24] proposed a decision-level fusion strategy based on Log-Gabor filter features. Using the Nearest Neighbor classifier, the scores were classified.…”