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 Spatio-Temporal Deep Learning for Face Liveness Detection: A Resnet-GRU Approach

Punam Chandrashekhar Wagale

Department: ComputerOrganization: Ajeenkya D Y Patil College of Engineering,Lohegaon, PuneEmail: pawar.punam08@gmail.com

Guide Name: Dr. Pankaj Agarkar.

Department: ComputerOrganization: Ajeenkya D Y Patil College of Engineering, Lohegaon, Pune.Email: pmagarkar@gmail.com

Abstract—Biometric authentication systems founded on face recognition are now embedded across a broad spectrum of real-world applications, making themhigh-value targets for spoofing attacks. Adversaries exploit artefacts ranging from two-dimensional printed photographs and replay video sequences to sculpted three-dimensional masks to deceive thesesystems. Countering such threats, face anti-spoofing  (FAS)—also termed presentation attack detection (PAD)—has emerged as an indispensable safeguardwithin modern authentication pipelines. This paper presents a spatio-temporal deep learning framew ork that fuses a ResNet-50 spatial encoder with a GatedRecurrent Unit (GRU) temporal module to  simultaneously capture liveness cues at both the texture and motion levels. Beyond the proposedsystem, a structured review of contemporary deep FAS methodologies is provided, covering pixel-w isesupervisory signals, domain-invarianttraining strategies, open-set evaluation protocols, and sensor-aware multi-modal architectures. Experimental results on a curated dataset of 1,250 samples yield aclassification accuracy of 96.2%, a false acceptancerate of 3.4%, and a false rejection rate of 3.8%,  outperforming several recently published baseline methods. Key open challenges and prospective research directions are identified to guide further development of robust, deployment-ready FAS systems.
Keywords—Face anti-spoofing; presentation attack detection; spatio-temporal deep learning; liveness detection; multi-modal generalization.