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Found 96 papers, 9 papers with code
Transporting Labels via Hierarchical Optimal Transport for Semi-Supervised Learning
Semi-Supervised Learning (SSL) based on Convolutional Neural Networks (CNNs) have recently been proven as powerful tools for standard tasks such as image classification when there is not a sufficient amount of labeled data available during the training.
Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis
The global spatial context is built upon the Transformer, which is specifically designed for image compression tasks.
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation
The transductive transfer learning (TTL) method that incorporates a CycleGAN-based unpaired domain translation network has been previously proposed in the literature for effective ATR annotation.
CATFace: Cross-Attribute-Guided Transformer with Self-Attention Distillation for Low-Quality Face Recognition
In this paper, we propose a novel multi-branch neural network that leverages SB attribute information to boost the performance of FR.
Neural-based Compression Scheme for Solar Image Data
In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs.
Trading-off Mutual Information on Feature Aggregation for Face Recognition
The consistent improvement we observed in these benchmarks demonstrates the efficacy of our approach in enhancing FR performance.
Multi-Context Dual Hyper-Prior Neural Image Compression
In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation.
Context-Aware Neural Video Compression on Solar Dynamics Observatory
NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity.
Multi-spectral Entropy Constrained Neural Compression of Solar Imagery
Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner.
Towards Generalizable Morph Attack Detection with Consistency Regularization
The proposed consistency regularization aligns the abstraction in the hidden layers of our model across the morph attack images which are generated from diverse domains in the wild.
CCFace: Classification Consistency for Low-Resolution Face Recognition
However, these methods have shown a significant decline in performance when applied to real-world low-resolution benchmarks like TinyFace or SCFace.
Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting
Deep convolutional neural networks have achieved remarkable success in face recognition (FR), partly due to the abundant data availability.
AAFACE: Attribute-aware Attentional Network for Face Recognition
In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task.
Frequency Disentangled Features in Neural Image Compression
The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code.
A Quality Aware Sample-to-Sample Comparison for Face Recognition
Our method adaptively finds and assigns more attention to the recognizable low-quality samples in the training datasets.
Deep Transductive Transfer Learning for Automatic Target Recognition
We employ a CycleGAN model to transfer the mid-wave infrared (MWIR) images to visible (VIS) domain images (or visible to MWIR domain).
Landmark Enforcement and Style Manipulation for Generative Morphing
Morph images threaten Facial Recognition Systems (FRS) by presenting as multiple individuals, allowing an adversary to swap identities with another subject.
Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory
We have designed an ad-hoc ANN-based image compression scheme to reduce the amount of data needed to be stored and retrieved on space missions studying solar dynamics.
Robust Ensemble Morph Detection with Domain Generalization
In this paper, we intend to learn a morph detection model with high generalization to a wide range of morphing attacks and high robustness against different adversarial attacks.
Pose Attention-Guided Profile-to-Frontal Face Recognition
Second, we develop a novel pose attention block (PAB) to specially guide the pose-agnostic feature extraction from profile faces.
Information Maximization for Extreme Pose Face Recognition
In this paper, we seek to draw connections between the frontal and profile face images in an abstract embedding space.
Revisiting Outer Optimization in Adversarial Training
This phenomenon hinders the outer optimization in AT since the convergence rate of MSGD is highly dependent on the variance of the gradients.
GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans
In this paper, we design a Generative Adversarial Network (GAN)-based solution for super-resolution and segmentation of optical coherence tomography (OCT) scans of the retinal layers.
Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training
Our ablation study results on the WVU-OCT data-set in five-fold cross-validation (5-CV) suggest that the proposed MultiSDGAN with a serial attention module provides the most competitive performance, and guiding the spatial attention feature maps by binary masks further improves the performance in our proposed network.
Quality-Aware Multimodal Biometric Recognition
The first loss assures that the representations of modalities for a class have comparable magnitudes to provide a better quality estimation, while the multimodal representations of different classes are distributed to achieve maximum discrimination in the embedding space.
Morph Detection Enhanced by Structured Group Sparsity
As such, instead of using images in the RGB domain, we decompose every image into its wavelet sub-bands using 2D wavelet decomposition and a deep supervised feature selection scheme is employed to find the most discriminative wavelet sub-bands of input images.
Human Age Estimation from Gene Expression Data using Artificial Neural Networks
In this line, we propose a new framework for human age estimation using information from human dermal fibroblast gene expression data.
Adversarially Perturbed Wavelet-based Morphed Face Generation
In this paper, we explore combination of two methods for morphed image generation, those of geometric transformation (warping and blending to create morphed images) and photometric perturbation.
Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition
In recent years, periocular recognition has been developed as a valuable biometric identification approach, especially in wild environments (for example, masked faces due to COVID-19 pandemic) where facial recognition may not be applicable.
Quality Map Fusion for Adversarial Learning
Generative adversarial models that capture salient low-level features which convey visual information in correlation with the human visual system (HVS) still suffer from perceptible image degradations.
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning
In few-shot classification, the primary goal is to learn representations from a few samples that generalize well for novel classes.
Deep adversarial attack on target detection systems
Target detection systems identify targets by localizing their coordinates on the input image of interest.
Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition
In the second approach, we design a coupled generative adversarial network (cpGAN) architecture consisting of a pair of cGAN modules that project the VIS and NIR iris images into a low-dimensional embedding domain to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject.
Attribute Guided Sparse Tensor-Based Model for Person Re-Identification
In this paper, we propose a new method to leverage features from human attributes for person ReID.
Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Prediction
To address this problem, we use a new Multi-Task Learning (MTL) paradigm in which a facial attribute predictor uses the knowledge of other related attributes to obtain a better generalization performance.
Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN
We have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN.
Attention Aware Wavelet-based Detection of Morphed Face Images
Morphed images have exploited loopholes in the face recognition checkpoints, e. g., Credential Authentication Technology (CAT), used by Transportation Security Administration (TSA), which is a non-trivial security concern.
Differential Morph Face Detection using Discriminative Wavelet Sub-bands
For some of the sub-bands, there is a marked difference between the entropy of the sub-band in a bona fide image and the identical sub-band's entropy in a morphed image.
FDeblur-GAN: Fingerprint Deblurring using Generative Adversarial Network
It is added to generate ridge maps to ensure that fingerprint information and minutiae are preserved in the deblurring process and prevent the model from generating erroneous minutiae.
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification
The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task.
Detection of Morphed Face Images Using Discriminative Wavelet Sub-bands
To detect morphing attacks, we propose a method which is based on a discriminative 2D Discrete Wavelet Transform (2D-DWT).
HGAN: Hybrid Generative Adversarial Network
However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse.
A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset
Thermal face imagery, which captures the naturally emitted heat from the face, is limited in availability compared to face imagery in the visible spectrum.
Matching Distributions via Optimal Transport for Semi-Supervised Learning
Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training.
Differential Morphed Face Detection Using Deep Siamese Networks
Although biometric facial recognition systems are fast becoming part of security applications, these systems are still vulnerable to morphing attacks, in which a facial reference image can be verified as two or more separate identities.
Mutual Information Maximization on Disentangled Representations for Differential Morph Detection
The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image.
Cross-Spectral Iris Matching Using Conditional Coupled GAN
Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals.
Attribute Adaptive Margin Softmax Loss using Privileged Information
We present a novel framework to exploit privileged information for recognition which is provided only during the training phase.
Efficient OCT Image Segmentation Using Neural Architecture Search
We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and pre-processed OCT image dataset.
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network
However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images.
PF-cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild
In this paper, we hypothesize that the profile face domain possesses a gradual connection with the frontal face domain in the deep feature space.
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval
The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correctingdecoder (NECD), which is an error correcting decoder implemented with a neural network.
SuperMix: Supervising the Mixing Data Augmentation
On the distillation task, solely classifying images mixed using the teacher's knowledge achieves comparable performance to the state-of-the-art distillation methods.
Boosting Deep Face Recognition via Disentangling Appearance and Geometry
We demonstrate that the proposed approach enhances the performance of deep face recognition models by assisting the training process in two ways.
Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces
In this work, we present a practical approach to the problem of facial landmark detection.
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
Deep neural networks are susceptible to adversarial manipulations in the input domain.
Identity-Aware Deep Face Hallucination via Adversarial Face Verification
First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions.
Deep Sparse Band Selection for Hyperspectral Face Recognition
In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands.
Defending Against Adversarial Iris Examples Using Wavelet Decomposition
Deep neural networks have presented impressive performance in biometric applications.
Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protection
The proposed architecture consists of two major components: a deep hashing (DH) component, which is used for robust mapping of face images to their corresponding intermediate binary codes, and a NND component, which corrects errors in the intermediate binary codes that are caused by differences in the enrollment and probe biometrics due to factors such as variation in pose, illumination, and other factors.
Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition
In this paper, we propose a novel attribute-guided cross-resolution (low-resolution to high-resolution) face recognition framework that leverages a coupled generative adversarial network (GAN) structure with adversarial training to find the hidden relationship between the low-resolution and high-resolution images in a latent common embedding subspace.
Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition
In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces.
Adversarial Examples to Fool Iris Recognition Systems
Therefore, to compensate for this shortcoming, we propose to train a deep auto-encoder surrogate network to mimic the conventional iris code generation procedure.
Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding
The MDHND consists of two separate modules: a multimodal deep hashing (MDH) module, which is used for feature-level fusion and binarization of multiple biometrics, and a neural network decoder (NND) module, which is used to refine the intermediate binary codes generated by the MDH and compensate for the difference between enrollment and probe biometrics (variations in pose, illumination, etc.).
Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval
With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community.
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound
These approaches usually fail to model domain-specific information which has no representation in the target domain.
Style and Content Disentanglement in Generative Adversarial Networks
This paper describes the Style and Content Disentangled GAN (SC-GAN), a new unsupervised algorithm for training GANs that learns disentangled style and content representations of the data.
Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis
In contrast to current unsupervised image-to-image translation techniques, our framework leverages a novel perceptual discriminator to learn the geometry of human face.
Fast Geometrically-Perturbed Adversarial Faces
The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications.
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition
This paper investigates the problem of aerial vehicle recognition using a text-guided deep convolutional neural network classifier.
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace.
ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction
We achieved the rank-10 accuracy of 88. 02\% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70. 89\% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching.
Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification
In this paper a novel cross-device text-independent speaker verification architecture is proposed.
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection
The low resolution of objects of interest in aerial images makes pedestrian detection and action detection extremely challenging tasks.
Generalized Bilinear Deep Convolutional Neural Networks for Multimodal Biometric Identification
We demonstrate that, rather than spatial fusion at the convolutional layers, the fusion can be performed on the outputs of the fully-connected layers of the modality-specific CNNs without any loss of performance and with significant reduction in the number of parameters.
Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification
Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification.
A Deep Face Identification Network Enhanced by Facial Attributes Prediction
In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance.
Attribute-Centered Loss for Soft-Biometrics Guided Face Sketch-Photo Recognition
Specifically, an attribute-centered loss is proposed which learns several distinct centers, in a shared embedding space, for photos and sketches with different combinations of attributes.
Attention-Based Guided Structured Sparsity of Deep Neural Networks
Network pruning is aimed at imposing sparsity in a neural network architecture by increasing the portion of zero-valued weights for reducing its size regarding energy-efficiency consideration and increasing evaluation speed.
Deep Cross Polarimetric Thermal-to-visible Face Recognition
we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind.
Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems.
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data
We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks.
Multibiometric Secure System Based on Deep Learning
In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding.
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
We propose the use of a coupled 3D Convolutional Neural Network (3D-CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features.
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speakers' utterances and creation of the speaker model.
Supervised Deep Sparse Coding Networks
In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding.
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow
Sparse representation-based classifiers have shown outstanding accuracy and robustness in image classification tasks even with the presence of intense noise and occlusion.
Learning to classify with possible sensor failures
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination
In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples.
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification
In this paper, we propose a supervised dictionary learning algorithm in the kernel domain for hyperspectral image classification.
Multimodal Task-Driven Dictionary Learning for Image Classification
Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms.
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints
We propose to enforce structured sparsity priors on the task-driven dictionary learning method in order to improve the performance of the hyperspectral classification.
Collaborative Multi-sensor Classification via Sparsity-based Representation
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations.
Quality-based Multimodal Classification Using Tree-Structured Sparsity
An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information.
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis.
Robust Lasso with missing and grossly corrupted observations
Our second set of results applies to a general class of Gaussian design matrix $X$ with i. i. d rows $\oper N(0, \Sigma)$, for which we provide a surprising phenomenon: the extended Lasso can recover exact signed supports of both $\beta^{\star}$ and $e^{\star}$ from only $\Omega(k \log p \log n)$ observations, even the fraction of corruption is arbitrarily close to one.
