Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features
Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features
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DOI：
10.1109/tifs.2015.2458700
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发表时间：
2015-11-01
影响因子：
6.8
通讯作者：
Christmas, William
中科院分区：
文献类型：
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作者：
Arashloo, Shervin Rahimzadeh;Kittler, Josef;Christmas, William
人脸识别在过去的几十年里一直是人们关注的焦点，因此在这一领域取得了重大进展。然而，欺骗攻击的问题可以挑战人脸生物识别系统在实际应用中。提出了一种基于核判别分析的人脸欺骗攻击方法。它的成功源于不同的创新。首先，它表明，最近提出的多尺度动态纹理描述符基于二值化的统计图像特征的三个正交平面（MBSIF-TOP）是有效的检测欺骗攻击，表现出良好的性能相比，现有的替代品。接下来，通过将MBSIF-TOP与模糊容忍描述符（即，动态多尺度局部相位量化（MLPQ-TOP）表示）相结合，可以进一步提高欺骗攻击检测器的鲁棒性。通过基于快速核判别分析（KDA）技术的核融合方法实现了MBSIF-TOP和MLPQ-TOP提供的信息的融合。它避免了昂贵的特征分析计算通过解决KDA问题，通过谱回归。在不同的数据库上进行的实验评估表明，与现有的方法相比，该系统在检测各种成像条件下的欺骗攻击方面具有优势。
Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this paper, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from different innovations. First, it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared with existing alternatives. Next, by combining MBSIF-TOP with a blur-tolerant descriptor, namely, the dynamic multiscale local phase quantization (MLPQ-TOP) representation, the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MBSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigen-analysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared with the existing methods.