Search Results for author:
Found 23 papers, 15 papers with code
Towards Zero-Shot Interpretable Human Recognition: A 2D-3D Registration Framework
However, three weaknesses are commonly reported for such kind of approaches: 1) their extreme demands in terms of learning data; 2) the difficulties in generalising between different domains; and 3) the lack of interpretability/explainability, with biometrics being of particular interest, as it is important to provide evidence able to be used for forensics/legal purposes (e. g., in courts).
Image-based human re-identification: Which covariates are actually (the most) important?
Human re-identification (re-ID) is nowadays among the most popular topics in computer vision, due to the increasing importance given to safety/security in modern societies.
Action Recognition
Image-To-Video Person Re-Identification
+4
Information retrieval: recent advances and beyond
This paper provides an extensive and thorough overview of the models and techniques utilized in the first and second stages of the typical information retrieval processing chain.
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses
Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing.
WASD: A Wilder Active Speaker Detection Dataset
The results show that: 1) AVA trained models maintain a state-of-the-art performance in WASD Easy group, while underperforming in the Hard one, showing the 2) similarity between AVA and Easy data; and 3) training in WASD does not improve models performance to AVA levels, particularly for audio impairment and surveillance settings.
Periocular Biometrics: A Modality for Unconstrained Scenarios
Periocular refers to the externally visible region of the face that surrounds the eye socket.
Deep Learning for Iris Recognition: A Survey
In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research.
Face Super-Resolution Using Stochastic Differential Equations
To the best of our knowledge, this is the first time SDEs have been used for such an application.
Generative Adversarial Graph Convolutional Networks for Human Action Synthesis
Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning).
Ranked #1 on
Human action generation
on Human3.6M
YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition
To overcome these limitations, we: 1) present frontal and wild face versions of three well-known surveillance datasets; and 2) propose YinYang-Net (YY-Net), a model that effectively and dynamically complements facial and body information, which makes it suitable for gender recognition in wild conditions.
REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition
It is known that the kinematics of the human body skeleton reveals valuable information in action recognition.
Is Gender "In-the-Wild" Inference Really a Solved Problem?
Soft biometrics analysis is seen as an important research topic, given its relevance to various applications.
Iterative weak/self-supervised classification framework for abnormal events detection
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Ranked #1 on
Semi-supervised Anomaly Detection
on UBI-Fights
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage
We propose a reversible face de-identification method for low resolution video data, where landmark-based techniques cannot be reliably used.
A Symbolic Temporal Pooling method for Video-based Person Re-Identification
In video-based person re-identification, both the spatial and temporal features are known to provide orthogonal cues to effective representations.
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices
Over the last decades, the world has been witnessing growing threats to the security in urban spaces, which has augmented the relevance given to visual surveillance solutions able to detect, track and identify persons of interest in crowds.
An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion.
A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification Problems
This paper describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i. e., when the response variables have dimension higher than one.
Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization
Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the quality of the obtained data.
Person Re-identification: Implicitly Defining the Receptive Fields of Deep Learning Classification Frameworks
The \emph{receptive fields} of deep learning classification models determine the regions of the input data that have the most significance for providing correct decisions.
Deep Representations for Cross-spectral Ocular Biometrics
One of the major challenges in ocular biometrics is the cross-spectral scenario, i. e., how to match images acquired in different wavelengths (typically visible (VIS) against near-infrared (NIR)).
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse.
Forensic shoe-print identification: a brief survey
As an advanced research topic in forensics science, automatic shoe-print identification has been extensively studied in the last two decades, since shoe marks are the clues most frequently left in a crime scene.
