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Found 26 papers, 3 papers with code
TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios
Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses.
Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB
Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image.
ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset
Following this intuition, in this paper we introduce ONOT, a synthetic dataset specifically focused on the generation of high-quality faces in adherence to the requirements of the ISO/IEC 39794-5 standards that, following the guidelines of the International Civil Aviation Organization (ICAO), defines the interchange formats of face images in electronic Machine-Readable Travel Documents (eMRTD).
Adversarial Identity Injection for Semantic Face Image Synthesis
Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.
Dealing with Subject Similarity in Differential Morphing Attack Detection
The advent of morphing attacks has posed significant security concerns for automated Face Recognition systems, raising the pressing need for robust and effective Morphing Attack Detection (MAD) methods able to effectively address this issue.
V-MAD: Video-based Morphing Attack Detection in Operational Scenarios
In response to the rising threat of the face morphing attack, this paper introduces and explores the potential of Video-based Morphing Attack Detection (V-MAD) systems in real-world operational scenarios.
SDFR: Synthetic Data for Face Recognition Competition
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
Enabling On-device Continual Learning with Binary Neural Networks
On-device learning remains a formidable challenge, especially when dealing with resource-constrained devices that have limited computational capabilities.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
3D Pose Nowcasting: Forecast the Future to Improve the Present
Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years.
Detecting Morphing Attacks via Continual Incremental Training
Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based training procedure, make the development of robust models particularly challenging.
On the challenges to learn from Natural Data Streams
In real-world contexts, sometimes data are available in form of Natural Data Streams, i. e. data characterized by a streaming nature, unbalanced distribution, data drift over a long time frame and strong correlation of samples in short time ranges.
SHREC 2022 Track on Online Detection of Heterogeneous Gestures
This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses.
Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps
Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes.
SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more.
Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face?
Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
Recently, deep learning approaches have achieved promising results in various fields of computer vision.
Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras
Event cameras are biologically-inspired sensors that gather the temporal evolution of the scene.
Learning to Generate Facial Depth Maps
In this paper, an adversarial architecture for facial depth map estimation from monocular intensity images is presented.
Face-from-Depth for Head Pose Estimation on Depth Images
Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only.
Head Detection with Depth Images in the Wild
Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images.
Learning to Map Vehicles into Bird's Eye View
Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies.
From Depth Data to Head Pose Estimation: a Siamese approach
In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner.
Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons
HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism.
Deep Head Pose Estimation from Depth Data for In-car Automotive Applications
Recently, deep learning approaches have achieved promising results in various fields of computer vision.
POSEidon: Face-from-Depth for Driver Pose Estimation
In this work, we present a new deep learning framework for head localization and pose estimation on depth images.
