The development of fair and robust remote photoplethysmography (rPPG) systems is fundamentally constrained by the lack of diversity in public datasets, especially a scarcity of data from individuals with the darkest skin phototypes (Fitzpatrick VI). To overcome this, this paper introduces two key contributions. First, the EquiNet-DB, a new large-scale video dataset that significantly extends previous benchmarks by adding a large cohort of new participants with a specific focus on Fitzpatrick skin types V and VI. Second, EquiNet, a novel neural architecture featuring a Joint Spatio-Temporal Attention (JSTA) mechanism designed to excel in low-SNR conditions. Trained and evaluated on the EquiNet-DB, the EquiNet model sets a new state-of-the-art, achieving unprecedented accuracy and demonstrating a dramatic reduction in performance disparity across skin tones. This work provides a foundational contribution towards reliable and equitable remote health monitoring.