29,684 research outputs found
DistancePPG: Robust non-contact vital signs monitoring using a camera
Vital signs such as pulse rate and breathing rate are currently measured
using contact probes. But, non-contact methods for measuring vital signs are
desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ
health tracking (e.g. on mobile phone and computers with webcams). Recently,
camera-based non-contact vital sign monitoring have been shown to be feasible.
However, camera-based vital sign monitoring is challenging for people with
darker skin tone, under low lighting conditions, and/or during movement of an
individual in front of the camera. In this paper, we propose distancePPG, a new
camera-based vital sign estimation algorithm which addresses these challenges.
DistancePPG proposes a new method of combining skin-color change signals from
different tracked regions of the face using a weighted average, where the
weights depend on the blood perfusion and incident light intensity in the
region, to improve the signal-to-noise ratio (SNR) of camera-based estimate.
One of our key contributions is a new automatic method for determining the
weights based only on the video recording of the subject. The gains in SNR of
camera-based PPG estimated using distancePPG translate into reduction of the
error in vital sign estimation, and thus expand the scope of camera-based vital
sign monitoring to potentially challenging scenarios. Further, a dataset will
be released, comprising of synchronized video recordings of face and pulse
oximeter based ground truth recordings from the earlobe for people with
different skin tones, under different lighting conditions and for various
motion scenarios.Comment: 24 pages, 11 figure
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body
shape from only a single photograph. Our model can infer full-body shape
including face, hair, and clothing including wrinkles at interactive
frame-rates. Results feature details even on parts that are occluded in the
input image. Our main idea is to turn shape regression into an aligned
image-to-image translation problem. The input to our method is a partial
texture map of the visible region obtained from off-the-shelf methods. From a
partial texture, we estimate detailed normal and vector displacement maps,
which can be applied to a low-resolution smooth body model to add detail and
clothing. Despite being trained purely with synthetic data, our model
generalizes well to real-world photographs. Numerous results demonstrate the
versatility and robustness of our method
Image quality-based adaptive illumination normalisation for face recognition
Automatic face recognition is a challenging task due to intra-class variations. Changes in lighting conditions during enrolment and identification stages contribute significantly to these intra-class variations. A common approach to address the effects such of varying conditions is to pre-process the biometric samples in order normalise intra-class variations. Histogram equalisation is a widely used illumination normalisation technique in face recognition. However, a recent study has shown that applying histogram equalisation on well-lit face images could lead to a decrease in recognition accuracy. This paper presents a dynamic approach to illumination normalisation, based on face image quality. The quality of a given face image is measured in terms of its luminance distortion by comparing this image against a known reference face image. Histogram equalisation is applied to a probe image if its luminance distortion is higher than a predefined threshold. We tested the proposed adaptive illumination normalisation method on the widely used Extended Yale Face Database B. Identification results demonstrate that our adaptive normalisation produces better identification accuracy compared to the conventional approach where every image is normalised, irrespective of the lighting condition they were acquired
A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos
This paper presents a comparative evaluation of methods for remote heart rate
estimation using face videos, i.e., given a video sequence of the face as
input, methods to process it to obtain a robust estimation of the subjects
heart rate at each moment. Four alternatives from the literature are tested,
three based in hand crafted approaches and one based on deep learning. The
methods are compared using RGB videos from the COHFACE database. Experiments
show that the learning-based method achieves much better accuracy than the hand
crafted ones. The low error rate achieved by the learning based model makes
possible its application in real scenarios, e.g. in medical or sports
environments.Comment: Accepted in "IEEE International Workshop on Medical Computing
(MediComp) 2020
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