  Estimation of the Heart rate from the facial video has a number of
applications in the medical and fitness industries. Additionally, it has become
useful in the field of gaming as well. Several approaches have been proposed to
seamlessly obtain the Heart rate from the facial video, but these approaches
have had issues in dealing with motion and illumination artifacts. In this
work, we propose a reliable HR estimation framework using the spectral
reflectance of the user, which makes it robust to motion and illumination
disturbances. We employ deep learning-based frameworks such as Faster RCNNs to
perform face detection as opposed to the Viola Jones algorithm employed by
previous approaches. We evaluate our method on the MAHNOB HCI dataset and found
that the proposed method is able to outperform previous approaches.Estimation
of the Heart rate from facial video has a number of applications in the medical
and the fitness industries. Additionally, it has become useful in the field of
gaming as well. Several approaches have been proposed to seamlessly obtain the
Heart rate from the facial video, but these approaches have had issues in
dealing with motion and illumination artifacts. In this work, we propose a
reliable HR estimation framework using the spectral reflectance of the user,
which makes it robust to motion and illumination disturbances. We employ deep
learning-based frameworks such as Faster RCNNs to perform face detection as
opposed to the Viola-Jones algorithm employed by previous approaches. We
evaluate our method on the MAHNOB HCI dataset and found that the proposed
method is able to outperform previous approaches.
