Search Results for author:
Found 34 papers, 4 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.
Synthetic Latent Fingerprint Generation Using Style Transfer
Limited data availability is a challenging problem in the latent fingerprint domain.
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.
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).
Super-resolution Guided Pore Detection for Fingerprint Recognition
High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.
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.
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.
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.
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.
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.
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.
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
Deep neural networks are susceptible to adversarial manipulations in the input domain.
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.
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.
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.
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.
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.
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.
