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Found 13 papers, 0 papers with code
Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids
This paper discusses the applications of deep learning (DL) to predict PF solutions for three-phase unbalanced power distribution grids.
Empirical Assessment of End-to-End Iris Recognition System Capacity
Iris is an established modality in biometric recognition applications including consumer electronics, e-commerce, border security, forensics, and de-duplication of identity at a national scale.
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.
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.
Deep Hashing for Secure Multimodal Biometrics
The efficacy of the proposed approach is shown using a multimodal database of face and iris and it is observed that the matching performance is improved due to the fusion of multiple biometrics.
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.
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.
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.
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.).
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
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.
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.
