no code implementations • 21 Jun 2023 • Katelyn M. Hampel, Jinyu Zuo, Priyanka Das, Natalia A. Schmid, Stephanie Schuckers, Joseph Skufca, Matthew C. Valenti
We determine the sustainable maximum population for each dataset based on the quality of the images.
no code implementations • 2 Nov 2021 • Veeru Talreja, Nasser M. Nasrabadi, Matthew C. Valenti
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.
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 • 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 • 3 Apr 2020 • Fariborz Taherkhani, Veeru Talreja, Matthew C. Valenti, Nasser M. Nasrabadi
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.
no code implementations • 5 Aug 2019 • Veeru Talreja, Matthew C. Valenti, Nasser M. Nasrabadi
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.
no code implementations • 5 Aug 2019 • Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
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.
no code implementations • 11 Feb 2019 • Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community.
no code implementations • 11 Feb 2019 • Veeru Talreja, Sobhan Soleymani, Matthew C. Valenti, Nasser M. Nasrabadi
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.).
no code implementations • 25 Oct 2017 • Veeru Talreja, Terry Ferrett, Matthew C. Valenti, Arun Ross
This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones.
no code implementations • 7 Aug 2017 • Veeru Talreja, Matthew C. Valenti, Nasser M. Nasrabadi
In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding.