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Found 34 papers, 5 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.
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.
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.
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.
Benchmarking Human Face Similarity Using Identical Twins
The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs.
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.
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 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.
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.
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).
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.
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.
Defending Against Adversarial Iris Examples Using Wavelet Decomposition
Deep neural networks have presented impressive performance in biometric applications.
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.).
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks
The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup.
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.
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.
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.
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.
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.
