Digital Identity Verification Using Liveness Detection Models

Abstract

Digital identity verification has become essential across online banking, e-governance, healthcare, education, and e-commerce. While biometric systems such as facial recognition, fingerprint scanning, and iris detection enhance convenience and scalability, they remain vulnerable to spoofing attacks including photo/video replays, deepfakes, silicone masks, and display-based presentation attacks. Liveness detection has therefore emerged as a critical security layer to distinguish genuine biometric traits from fraudulent representations. This paper presents a comprehensive study of liveness detection models, covering architecture, algorithms, and performance evaluation. It examines active and passive techniques, including texture analysis, motion cues, physiological signal extraction, and deep learning approaches. Traditional handcrafted features are compared with CNNs, RNNs, and transformer-based models. Emphasis is placed on multimodal biometrics and challenge-response mechanisms to counter advanced GAN-based deepfake attacks. A systematic framework is proposed, encompassing data acquisition, preprocessing, feature extraction, model training, and decision fusion, supported by mathematical formulations for classification, loss optimization, and evaluation metrics. Experimental results on benchmark datasets demonstrate improvements in accuracy, FAR, and APCER, with hybrid deep learning models integrating temporal and physiological cues outperforming single-modality methods. The paper concludes by addressing deployment challenges, ethical considerations, and future directions such as privacy-preserving learning, federated identity systems, and explainable AI security, offering a scalable approach for secure digital identity verification.