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Face spoofing detection ensemble via multistage optimisation and pruning
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AI
DOI：10.1016/j.patrec.2022.04.006.png)
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Abstract
En 中文
Despite the recent improvements in facial recognition, face spoofing attacks can still pose a serious security threat to biometric systems. As fraudsters are coming up with novel spoofing attacks, anomaly-based detectors, compared to the binary spoofing attack counterparts, have certain generalisation performance advantages. In this work, we investigate the merits of fusing multiple anomaly classifiers using weighted averaging (WA) fusion. The design of the entire system is based on genuine-access data only. To optimise the parameters of WA, we propose a novel three-stage optimisation method with the following contributions: (a) A new hybrid optimisation method using Genetic Algorithm (GA) and Pattern Search (PS) to explore the weight space more effectively (b) a novel two-sided score normalisation method to improve the anomaly detection performance (c) a new ensemble pruning method to improve the generalisation performance. To further boost the performance of the proposed anomaly detection ensemble, we incorporate client-specific information to train the proposed model. We evaluate the capability of the proposed model on publicly available face spoofing databases including Replay-Attack, Replay-Mobile and Rose-Youtu. The experimental results demonstrate that the proposed WA fusion outperforms the stateof-the-art anomaly-based and multiclass approaches.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Face spoofing detection
Anomaly detection
Client-specific information
Ensemble of one-class classifiers
Convolutional neural networks
Ensemble pruning
Face spoofing detection
Anomaly detection
Client-specific information
Ensemble of one-class classifiers
Convolutional neural networks
Ensemble pruning
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