Comparison of Independent Component Analysis (ICA) Algorithm for Heart Rate Measurement Based on Facial Imaging

This paper deals with the heart rate measurement by performing automatic face tracking and blind source separation of three color channel into independent components. A class of the so-called Independent Component Analysis (ICA) represents a powerful tool for such a detection. Various ICA algorithms have been introduces in the literature; therefore there is a need to compare these methods. In this contribution, two of the most common ICA methods are studied and compared to each other as regarding their ability to recover the independent source signal from normalized RGB of the facial image. These are the Joint Approximate Diagonalization of Eigen matrices (JADE), and the Second Order Blind Identification (SOBI). These two algorithms have been applied to the same data set of RGB traces then compare with commercially calibrated BPV sensor. Both two methods have given approximately consistent results. However SOBI method has shown better accuration of heart rate measurement over JADE.