This work addresses the problem of recovering the morphology of blood volume pulse (BVP) information from a video of a person's face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://github.com/yogeshd21/CVPM-2023-iPPG-Paper. / Master of Science / In this work we are trying to study and recover human health related metrics and the physiological signals which are at the core for the derivation of such metrics. A well know form of physiological signals is ECG (Electrocardiogram) signals and for our research we work with BVP (Blood Volume Pulse) signals. With this work we are proposing a Deep Learning based model for non-invasive retrieval of human physiological signals from human face videos. Most of the state of the art models as well as researchers try to recover averaged cardiac pulse based metrics like heart rate, breathing rate, etc. without focusing on the details of the recovered physiological signal. Physiological signals like BVP have details like systolic peak, diastolic peak and dicrotic notch, and these signals also have applications in various domains like human mental health study, emotional stimuli study, etc. Hence with this work we focus on retrieval of the morphology of such physiological signals and present a quantitative as well as qualitative results for the same. An efficient attention based deep learning model is presented and scope of reidentification using the retrieved signals is also explored. Along with significant implementations like skin detection model our proposed architecture also shows better performance than state of the art models for two very challenging datasets UBFC-Phys as well as COHFACE. The source code is available at https://github.com/yogeshd21/CVPM-2023-iPPG-Paper.

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.