  Recent advances in remote heart rate measurement, motivated by data-driven
approaches, have notably enhanced accuracy. However, these improvements
primarily focus on recovering the rPPG signal, overlooking the implicit
challenges of estimating the heart rate (HR) from the derived signal. While
many methods employ the Fast Fourier Transform (FFT) for HR estimation, the
performance of the FFT is inherently affected by a limited frequency
resolution. In contrast, the Chirp-Z Transform (CZT), a generalization form of
FFT, can refine the spectrum to the narrow-band range of interest for heart
rate, providing improved frequential resolution and, consequently, more
accurate estimation. This paper presents the advantages of employing the CZT
for remote HR estimation and introduces a novel data-driven adaptive CZT
estimator. The objective of our proposed model is to tailor the CZT to match
the characteristics of each specific dataset sensor, facilitating a more
optimal and accurate estimation of HR from the rPPG signal without compromising
generalization across diverse datasets. This is achieved through a Sparse
Matrix Optimization (SMO). We validate the effectiveness of our model through
exhaustive evaluations on three publicly available datasets UCLA-rPPG, PURE,
and UBFC-rPPG employing both intra- and cross-database performance metrics. The
results reveal outstanding heart rate estimation capabilities, establishing the
proposed approach as a robust and versatile estimator for any rPPG method.
