Seeing Red: PPG Biometrics Using Smartphone Cameras
- URL: http://arxiv.org/abs/2004.07088v1
- Date: Wed, 15 Apr 2020 13:50:36 GMT
- Title: Seeing Red: PPG Biometrics Using Smartphone Cameras
- Authors: Giulio Lovisotto, Henry Turner, Simon Eberz and Ivan Martinovic
- Abstract summary: We propose a system that enables photoplethysmogram-based authentication by using a smartphone camera.
PPG signals are obtained by recording a video from the camera as users are resting their finger on top of the camera lens.
- Score: 20.911850979477236
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a system that enables photoplethysmogram
(PPG)-based authentication by using a smartphone camera. PPG signals are
obtained by recording a video from the camera as users are resting their finger
on top of the camera lens. The signals can be extracted based on subtle changes
in the video that are due to changes in the light reflection properties of the
skin as the blood flows through the finger. We collect a dataset of PPG
measurements from a set of 15 users over the course of 6-11 sessions per user
using an iPhone X for the measurements. We design an authentication pipeline
that leverages the uniqueness of each individual's cardiovascular system,
identifying a set of distinctive features from each heartbeat. We conduct a set
of experiments to evaluate the recognition performance of the PPG biometric
trait, including cross-session scenarios which have been disregarded in
previous work. We found that when aggregating sufficient samples for the
decision we achieve an EER as low as 8%, but that the performance greatly
decreases in the cross-session scenario, with an average EER of 20%.
Related papers
- User Authentication and Vital Signs Extraction from Low-Frame-Rate and Monochrome No-contact Fingerprint Captures [0.8795040582681392]
We leverage low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users.
Preliminary results are promising, with low error rates for both heart rate estimation and user authentication.
arXiv Detail & Related papers (2024-12-10T00:47:36Z) - Multimodal Biometric Authentication Using Camera-Based PPG and Fingerprint Fusion [10.360896128201237]
This paper presents a multimodal biometric system that integrates PPG signals extracted from videos with fingerprint data to enhance the accuracy of user verification.
System requires users to place their fingertip on the camera lens for a few seconds, allowing the capture and processing of unique biometric characteristics.
arXiv Detail & Related papers (2024-12-07T14:09:40Z) - Full-Body Cardiovascular Sensing with Remote Photoplethysmography [4.123458880886283]
Remote photoplethysmography (r) allows for noncontact monitoring of blood volume changes from a camera by detecting minor fluctuations in reflected light.
We explored the feasibility of r from non-face body regions such as the arms, legs, and hands.
arXiv Detail & Related papers (2023-03-16T20:37:07Z) - Facial Soft Biometrics for Recognition in the Wild: Recent Works,
Annotation, and COTS Evaluation [63.05890836038913]
We study the role of soft biometrics to enhance person recognition systems in unconstrained scenarios.
We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems.
Experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning.
arXiv Detail & Related papers (2022-10-24T11:29:57Z) - Compact multi-scale periocular recognition using SAFE features [63.48764893706088]
We present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor.
We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this point unique of the eye.
arXiv Detail & Related papers (2022-10-18T11:46:38Z) - Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits.
Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases.
In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
arXiv Detail & Related papers (2022-03-14T17:05:59Z) - A Comparative Study of Fingerprint Image-Quality Estimation Methods [54.84936551037727]
Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system.
In this work, we review existing approaches for fingerprint image-quality estimation.
We have also tested a selection of fingerprint image-quality estimation algorithms.
arXiv Detail & Related papers (2021-11-14T19:53:12Z) - HighlightMe: Detecting Highlights from Human-Centric Videos [52.84233165201391]
We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos.
We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions.
We observe a 4-12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods.
arXiv Detail & Related papers (2021-10-05T01:18:15Z) - Real Time Video based Heart and Respiration Rate Monitoring [5.257115841810259]
Smartphone cameras can measure heart rate (HR) and respiration rate (RR)
variation in the intensity of the green channel can be measured by the i signals of the video.
This study aimed to provide a method to extract heart rate and respiration rate using the video of individuals' faces.
arXiv Detail & Related papers (2021-06-04T19:03:21Z) - One-shot Representational Learning for Joint Biometric and Device
Authentication [14.646962064352577]
We propose a method to simultaneously perform (i.e., identify the individual) and device recognition from a single biometric image.
Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy.
arXiv Detail & Related papers (2021-01-02T22:29:29Z) - Towards End-to-end Video-based Eye-Tracking [50.0630362419371]
Estimating eye-gaze from images alone is a challenging task due to un-observable person-specific factors.
We propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships.
We demonstrate that the fusion of information from visual stimuli as well as eye images can lead towards achieving performance similar to literature-reported figures.
arXiv Detail & Related papers (2020-07-26T12:39:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.