This thesis considers methods for extracting blood volume pulse (BVP) representations from video of the human face. Whereas most previous systems have been concerned with estimating vital signs such as average heart rate, this thesis addresses the more difficult problem of recovering BVP signal morphology. We present a new approach that is inspired by temporal encoder-decoder architectures that have been used for audio signal separation. As input, this system accepts a temporal sequence of RGB (red, green, blue) values that have been spatially averaged over a small portion of the face. The output of the system is a temporal sequence that approximates a BVP signal. In order to reduce noise in the recovered signal, a separate processing step extracts individual pulses and performs normalization and outlier removal. After these steps, individual pulse shapes have been extracted that are sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area. The source code is available at https://github.com/Adleof/CVPM-2023-Temporal-Encoder-Decoder-iPPG / Master of Science / This thesis considers methods for extracting blood volume pulse (BVP) representations from video of the human face. We present a new approach that is inspired by the method that has been used for audio signal separation. The output of our system is an approximation of the BVP signal of the person in the video. Our method can extract a signal that is sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area.

This thesis introduces a method for extracting blood volume pulse (BVP) signals from facial videos, moving beyond basic heart rate estimation to capture full pulse waveforms. Our approach adapts techniques from audio signal separation and applies them to video, using a machine learning model capable of processing complex time-based data. By incorporating both regular RGB (red, green, blue) and infrared (850nm, 940nm) video, we enhance the quality of the extracted signals, making signal extraction more reliable under different lighting conditions. This method not only improves accuracy in measuring real-time heart rate but also captures unique heart patterns that could support biometric identification. Our findings show that this approach effectively recovers detailed BVP shapes from video, paving the way for advancements in health monitoring and identity verification technologies. / Master of Science / This thesis focuses on how to measure heart signals from facial videos in a way that captures more detail than just average heart rate. We use a machine learning model designed for an audio separation task, adapting it to separate blood flow signals from noise in signals extracted from video of the face. By adding infrared video data along with regular color channels, our method becomes more accurate, especially in low-light situations. This allows us to not only calculate a person's heart rate more precisely but also to create unique patterns from their heartbeat, which could help in personal identification. Through testing, we show that our method can successfully extract clear heart signals from video, opening up new uses for health monitoring and security.