This study investigates the impact of face image background correction through segmentation on face recognition and morphing attack detection performance in realistic, unconstrained image capture scenarios. The motivation is driven by operational biometric systems such as the European Entry/Exit System (EES), which require facial enrolment at airports and other border crossing points where controlled backgrounds usually required for such captures cannot always be guaranteed, as well as by accessibility needs that may necessitate image capture outside traditional office environments. By analyzing how such preprocessing steps influence both recognition accuracy and security mechanisms, this work addresses a critical gap between usability-driven image normalization and the reliability requirements of large-scale biometric identification systems. Our study evaluates a comprehensive range of segmentation techniques, three families of morphing attack detection methods, and four distinct face recognition models, using databases that include both controlled and in-the-wild image captures. The results reveal consistent patterns linking segmentation to both recognition performance and face image quality. Additionally, segmentation is shown to systematically influence morphing attack detection performance. These findings highlight the need for careful consideration when deploying such preprocessing techniques in operational biometric systems.

翻译：本研究探究在真实、非受控图像采集场景下，通过分割技术进行人脸图像背景校正对人脸识别与形态攻击检测性能的影响。研究动机源于欧洲出入境系统等实际运行中的生物特征系统——这些系统在机场及其他边境口岸要求进行人脸注册，但受控背景条件通常无法得到保证，同时考虑无障碍需求可能需要在传统办公环境之外进行图像采集。通过分析此类预处理步骤对识别精度与安全机制的双重影响，本研究填补了可用性驱动的图像标准化与大规模生物特征识别系统可靠性需求之间的关键空白。我们评估了涵盖多种分割技术、三代形态攻击检测方法及四种不同人脸识别模型的综合方案，使用的数据库同时包含受控环境与野外采集的人脸图像。实验结果揭示了分割操作与人脸识别性能及图像质量之间的一致关联模式。此外，分割过程被证明会系统性地影响形态攻击检测性能。这些发现表明，在运行生物特征系统中部署此类预处理技术时需审慎考量。