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Found 15 papers, 5 papers with code
Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)
The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications.
Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions
The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch.
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Signal analysis and classification is fraught with high levels of noise and perturbation.
From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range
This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters.
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models.
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems
Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development.
The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition
Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur.
Automatic Discovery of Political Meme Genres with Diverse Appearances
This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres.
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks.
Beyond Pixels: Image Provenance Analysis Leveraging Metadata
Creative works, whether paintings or memes, follow unique journeys that result in their final form.
Image Provenance Analysis at Scale
Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images.
Provenance Filtering for Multimedia Phylogeny
Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time.
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media.
Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows.
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step?
