Trade-off Between Spatial and Angular Resolution in Facial Recognition
This addresses computational limitations in face recognition for applications requiring robustness, but it is incremental as it builds on existing light field methods.
This paper tackles the problem of improving face recognition performance by exploring the trade-off between spatial and angular resolution in light field-based systems, showing that increasing angular resolution up to a point enhances results at the expense of spatial resolution.
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.