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Found 31 papers, 6 papers with code
SynCDR : Training Cross Domain Retrieval Models with Synthetic Data
In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains.
Recognizing People by Body Shape Using Deep Networks of Images and Words
Although the non-linguistic model yielded fewer false accepts at all distances, fusion of the linguistic and non-linguistic models decreased false accepts for all, but the UAV images.
A Brief Survey on Person Recognition at a Distance
Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras.
Twin identification over viewpoint change: A deep convolutional neural network surpasses humans
These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.
Where in the World is this Image? Transformer-based Geo-localization in the Wild
Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem.
Ranked #3 on
Photo geolocation estimation
on Im2GPS3k
The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks
Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs.
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
In D&D, we train a teacher network on images from one category of an attribute; e. g. light skintone.
A Synthesis-Based Approach for Thermal-to-Visible Face Verification
Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition
We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface.
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks.
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes.
Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?
We discuss data driven factors (e. g., image quality, image population statistics, and algorithm architecture), and scenario modeling factors that consider the role of the "user" of the algorithm (e. g., threshold decisions and demographic constraints).
How are attributes expressed in face DCNNs?
In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw.
Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition
In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph.
On measuring the iconicity of a face
For a given identity in a face dataset, there are certain iconic images which are more representative of the subject than others.
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)
Subjects were tested subsequently on their ability to recognize those identities in low-resolution videos depicting the drivers operating a motor vehicle.
Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed
Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons.
An Automatic System for Unconstrained Video-Based Face Recognition
In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames.
A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos
In this paper, we present a modular system for spatio-temporal action detection in untrimmed security videos.
A Fast and Accurate System for Face Detection, Identification, and Verification
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification
In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance.
SfSNet: Learning Shape, Reflectance and Illuminance of Faces `in the Wild'
SfSNet learns from a mixture of labeled synthetic and unlabeled real world images.
Deep Density Clustering of Unconstrained Faces
In this paper, we consider the problem of grouping a collection of unconstrained face images in which the number of subjects is not known.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
Improving Network Robustness against Adversarial Attacks with Compact Convolution
In particular, we show that learning features in a closed and bounded space improves the robustness of the network.
SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild
SfSNet learns from a mixture of labeled synthetic and unlabeled real world images.
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Domain Adaptation is an actively researched problem in Computer Vision.
Ranked #27 on
Domain Adaptation
on Office-31
L2-constrained Softmax Loss for Discriminative Face Verification
In recent years, the performance of face verification systems has significantly improved using deep convolutional neural networks (DCNNs).
Ranked #4 on
Face Verification
on IJB-A
An All-In-One Convolutional Neural Network for Face Analysis
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.
Ranked #9 on
Face Verification
on IJB-A
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.
