  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.
