no code implementations • ECCV 2020 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 20 Nov 2024 • Shoaib Meraj Sami, Md Mahedi Hasan, Jeremy Dawson, Nasser Nasrabadi
During the training phase, we impose to preserve the high-frequency features of super-resolved and ground truth (GT) images that improve the SR image quality during inference.
no code implementations • 22 Jul 2024 • David Keaton, Amol S. Joshi, Jeremy Dawson, Nasser M. Nasrabadi
The challenge of deblurring fingerphoto images, or generating a sharp fingerphoto from a given blurry one, is a significant problem in the realm of computer vision.
no code implementations • 15 Jul 2024 • Amol S. Joshi, Ali Dabouei, Jeremy Dawson, Nasser Nasrabadi
Quality assessment of fingerprints captured using digital cameras and smartphones, also called fingerphotos, is a challenging problem in biometric recognition systems.
no code implementations • 27 Sep 2023 • Amol S. Joshi, Ali Dabouei, Nasser Nasrabadi, Jeremy Dawson
Limited data availability is a challenging problem in the latent fingerprint domain.
no code implementations • 25 Aug 2022 • Shoaib Meraj Sami, John McCauley, Sobhan Soleymani, Nasser Nasrabadi, Jeremy Dawson
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.
no code implementations • 10 Dec 2021 • Sobhan Soleymani, Ali Dabouei, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 29 Nov 2021 • Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 3 Nov 2021 • Kelsey O'Haire, Sobhan Soleymani, Baaria Chaudhary, Poorya Aghdaie, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 3 Aug 2021 • Moktari Mostofa, Salman Mohamadi, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 29 Jul 2021 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
In this paper, we propose a new method to leverage features from human attributes for person ReID.
no code implementations • 29 Jul 2021 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 29 Jul 2021 • Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi
We have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN.
no code implementations • 29 Jun 2021 • Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 24 Jun 2021 • Baaria Chaudhary, Poorya Aghdaie, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 21 Jun 2021 • Amol S. Joshi, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • CVPR 2021 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 16 Jun 2021 • Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
To detect morphing attacks, we propose a method which is based on a discriminative 2D Discrete Wavelet Transform (2D-DWT).
no code implementations • 10 Dec 2020 • Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson, Nasser M Nasrabadi
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.
no code implementations • 4 Dec 2020 • Fariborz Taherkhani, Hadi Kazemi, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 2 Dec 2020 • Sobhan Soleymani, Baaria Chaudhary, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 2 Dec 2020 • Sobhan Soleymani, Ali Dabouei, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 9 Oct 2020 • Moktari Mostofa, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals.
no code implementations • 25 Apr 2020 • Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi
In this paper, we hypothesize that the profile face domain possesses a gradual connection with the frontal face domain in the deep feature space.
no code implementations • 13 Jan 2020 • Ali Dabouei, Fariborz Taherkhani, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
We demonstrate that the proposed approach enhances the performance of deep face recognition models by assisting the training process in two ways.
1 code implementation • 8 Oct 2019 • Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
Deep neural networks are susceptible to adversarial manipulations in the input domain.
no code implementations • 15 Aug 2019 • Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 8 Aug 2019 • Sobhan Soleymani, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
Deep neural networks have presented impressive performance in biometric applications.
no code implementations • 21 Jun 2019 • Sobhan Soleymani, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
Therefore, to compensate for this shortcoming, we propose to train a deep auto-encoder surrogate network to mimic the conventional iris code generation procedure.
1 code implementation • 24 Sep 2018 • Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 31 Jul 2018 • Sobhan Soleymani, Ali Dabouei, Seyed Mehdi Iranmanesh, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
In this paper a novel cross-device text-independent speaker verification architecture is proposed.
no code implementations • 31 Jul 2018 • Ali Dabouei, Sobhan Soleymani, Hadi Kazemi, Seyed Mehdi Iranmanesh, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 3 Jul 2018 • Sobhan Soleymani, Ali Dabouei, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification.
no code implementations • 3 Jul 2018 • Sobhan Soleymani, Amirsina Torfi, Jeremy Dawson, Nasser M. Nasrabadi
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.
no code implementations • 20 Apr 2018 • Fariborz Taherkhani, Nasser M. Nasrabadi, Jeremy Dawson
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.
2 code implementations • 18 Jun 2017 • Amirsina Torfi, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi, Jeremy Dawson
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.
5 code implementations • 26 May 2017 • Amirsina Torfi, Jeremy Dawson, Nasser M. Nasrabadi
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.