Ensuring robustness in face recognition systems across various challenging conditions is crucial for their versatility. State-of-the-art methods often incorporate additional information, such as depth, thermal, or angular data, to enhance performance. However, light field-based face recognition approaches that leverage angular information face computational limitations. This paper investigates the fundamental trade-off between spatio-angular resolution in light field representation to achieve improved face recognition performance. By utilizing macro-pixels with varying angular resolutions while maintaining the overall image size, we aim to quantify the impact of angular information at the expense of spatial resolution, while considering computational constraints. Our experimental results demonstrate a notable performance improvement in face recognition systems by increasing the angular resolution, up to a certain extent, at the cost of spatial resolution.

翻译：确保人脸识别系统在多种复杂条件下的鲁棒性对其通用性至关重要。当前最先进的方法通常引入深度、热成像或角度数据等附加信息以提升性能。然而，基于光场的角度信息人脸识别方法面临计算资源的限制。本文研究了光场表示中空间-角度分辨率之间的基本权衡，以优化人脸识别性能。通过采用不同角度分辨率的宏像素（同时保持图像总尺寸不变），我们旨在量化在牺牲空间分辨率的条件下角度信息的影响，同时考虑计算约束。实验结果表明，通过在一定程度内以牺牲空间分辨率为代价提高角度分辨率，人脸识别系统的性能可获得显著提升。