3sXcsNet: A framework for face presentation attack detection using deep learning

Abstract

Face Recognition is a widely used authentication method deployed for accessing personal digital devices like mobiles and laptops, digital door locks, or various security applications. However, presentation attacks in form of print, video and mask can circumvent such authentication methods. Several solutions based on texture features are proposed in the state-of-the-art; nevertheless, they are sensitive to varying illumination conditions and are less resilient against 2D and 3D attacks. In this paper, we propose a novel 3-stream compact Xception framework (3sXcsNet) consisting of four input streams RGB, MSRCR, MSRCP, and a depth map to form two specific 3-stream fusion (CRF and CPF) to mitigate the illumination scenarios. These streams provide discriminative features. RGB images have rich texture features; MSRCR and MSRCP images are illumination invariant and preserve better chromaticity applied on a color or intensity channel, respectively. In addition, an auxiliary depth map that provides depth information for each pixel makes it easier to analyze features and recognize whether the input image is from a real person or a spoof medium. The 3-stream CRF and CPF represent the features differently and feed independently to a deep CNN compact Xception with a CBAM network. These feature maps are fused using an average fusion mechanism for classification of real and spoof faces. We utilize both 2D and 3D attack datasets namely, REPLAY-ATTACK, CASIA-FASD, OULU-NPU, and 3DMAD to evaluate the effectiveness of the proposed framework. We attain an EER of 1.27% and 0.19% for CRF fusion, as well as 1.48% and 0.20% for CPF fusion when assessing performance on the CASIA-FASD and REPLAY-ATTACK datasets, respectively. On the OULU-NPU dataset, we obtain the most favorable results for two protocols while employing CRF fusion, resulting in ACER metrics of 0.7% and 3.2±2.7%, respectively. On the other hand, CPF fusion outperformed with an ACER of 1.8±1.0% for one protocol. Our findings on intra-database outperform the current state-of-the-art. We also perform cross-database testing to assess the generalization capability of the framework on 2D and 3D attack datasets. Furthermore, the proposed method outperforms both fusion procedures in terms of error rate HTER for intra-database and cross-database testing on the 3DMAD dataset.