A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset
- URL: http://arxiv.org/abs/2101.02637v1
- Date: Thu, 7 Jan 2021 17:17:12 GMT
- Title: A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset
- Authors: Domenick Poster, Matthew Thielke, Robert Nguyen, Srinivasan Rajaraman,
Xing Di, Cedric Nimpa Fondje, Vishal M. Patel, Nathaniel J. Short, Benjamin
S. Riggan, Nasser M. Nasrabadi, Shuowen Hu
- Abstract summary: We present the DEVCOM Army Research Laboratory Visible-Thermal Face dataset (ARL-VTF)
With over 500,000 images from 395 subjects, the ARL-VTF dataset represents to the best of our knowledge, the largest collection of paired visible and thermal face images to date.
This paper presents benchmark results and analysis on thermal face landmark detection and thermal-to-visible face verification by evaluating state-of-the-art models on the ARL-VTF dataset.
- Score: 62.193924313292875
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Thermal face imagery, which captures the naturally emitted heat from the
face, is limited in availability compared to face imagery in the visible
spectrum. To help address this scarcity of thermal face imagery for research
and algorithm development, we present the DEVCOM Army Research Laboratory
Visible-Thermal Face Dataset (ARL-VTF). With over 500,000 images from 395
subjects, the ARL-VTF dataset represents, to the best of our knowledge, the
largest collection of paired visible and thermal face images to date. The data
was captured using a modern long wave infrared (LWIR) camera mounted alongside
a stereo setup of three visible spectrum cameras. Variability in expressions,
pose, and eyewear has been systematically recorded. The dataset has been
curated with extensive annotations, metadata, and standardized protocols for
evaluation. Furthermore, this paper presents extensive benchmark results and
analysis on thermal face landmark detection and thermal-to-visible face
verification by evaluating state-of-the-art models on the ARL-VTF dataset.
Related papers
- Learning Domain and Pose Invariance for Thermal-to-Visible Face
Recognition [6.454199265634863]
We propose a novel Domain and Pose Invariant Framework that simultaneously learns domain and pose invariant representations.
Our proposed framework is composed of modified networks for extracting the most correlated intermediate representations from off-pose thermal and frontal visible face imagery.
Although DPIF focuses on learning to match off-pose thermal to frontal visible faces, we also show that DPIF enhances performance when matching frontal thermal face images to frontal visible face images.
arXiv Detail & Related papers (2022-11-17T05:24:02Z) - A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in
Various Environment Conditions and Distances From The Camera [3.2872586139884623]
This article presents a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace.
Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions.
The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image.
arXiv Detail & Related papers (2022-04-29T17:57:54Z) - Thermal to Visible Image Synthesis under Atmospheric Turbulence [67.99407460140263]
In biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime conditions.
Such imaging systems often suffer from atmospheric turbulence, which introduces severe blur and deformation artifacts to the captured images.
An end-to-end reconstruction method is proposed which can directly transform thermal images into visible-spectrum images.
arXiv Detail & Related papers (2022-04-06T19:47:41Z) - A Synthesis-Based Approach for Thermal-to-Visible Face Verification [105.63410428506536]
This paper presents an algorithm that achieves state-of-the-art performance on the ARL-VTF and TUFTS multi-spectral face datasets.
We also present MILAB-VTF(B), a challenging multi-spectral face dataset composed of paired thermal and visible videos.
arXiv Detail & Related papers (2021-08-21T17:59:56Z) - Simultaneous Face Hallucination and Translation for Thermal to Visible
Face Verification using Axial-GAN [74.22129648654783]
We introduce the task of thermal-to-visible face verification from low-resolution thermal images.
We propose Axial-Generative Adversarial Network (Axial-GAN) to synthesize high-resolution visible images for matching.
arXiv Detail & Related papers (2021-04-13T22:34:28Z) - Exploring Thermal Images for Object Detection in Underexposure Regions
for Autonomous Driving [67.69430435482127]
Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving.
The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals.
This work proposes a domain adaptation framework which employs a style transfer technique for transfer learning from visible spectrum images to thermal images.
arXiv Detail & Related papers (2020-06-01T09:59:09Z) - Multi-Scale Thermal to Visible Face Verification via Attribute Guided
Synthesis [55.29770222566124]
We use attributes extracted from visible images to synthesize attribute-preserved visible images from thermal imagery for cross-modal matching.
A novel multi-scale generator is proposed to synthesize the visible image from the thermal image guided by the extracted attributes.
A pre-trained VGG-Face network is leveraged to extract features from the synthesized image and the input visible image for verification.
arXiv Detail & Related papers (2020-04-20T01:45:05Z)
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.