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Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification [article]

Sobhan Soleymani, Ali Dabouei, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
2018 arXiv   pre-print
We show that our deep multi-modal CNNs with multimodal fusion at several different feature level abstraction can significantly outperform the unimodal representation accuracy.  ...  We demonstrate an increase in multimodal person identification performance by utilizing the proposed multi-level feature abstract representations in our multimodal fusion, rather than using only the features  ...  ACKNOWLEDGEMENT This work is based upon a work supported by the Center for Identification Technology Research and the National Science Foundation under Grant #1650474.  ... 
arXiv:1807.01332v1 fatcat:eyjp3abfcje6hdltrvcaidzzji

A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods

Jucheng Yang, Wenhui Sun, Na Liu, Yarui Chen, Yuan Wang, Shujie Han
2018 Symmetry  
First, it learns the hidden-layer representation of biological images using extreme learning machines layer by layer.  ...  This study proposes a novel multimodal biometrics recognition model based on the stacked extreme learning machines (ELMs) and canonical correlation analysis (CCA) methods.  ...  [5] learned multimodal data expression via the Deep Belief Network model, which mapped multi-data to a joint hidden expression layer and defined a joint density model on the multimodal input space to  ... 
doi:10.3390/sym10040096 fatcat:peyzqnio75cl7ngg4lmlipm54e

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

K. Gunasekaran, J. Raja, R. Pitchai
2019 Automatika  
Finally, a deep learning framework is presented for improving the recognition rate of the multimodal biometric system in temporal domain.  ...  This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris.  ...  A general formulation based on Low-Rank and Joint Sparse Representations (LR-JSR) for Multi-Modal Recognition was presented in [1] .  ... 
doi:10.1080/00051144.2019.1565681 fatcat:ziqkw7iw6veabakdsoo5j7vh3i

Guest Editorial Introduction to the Special Section on Intelligent Visual Content Analysis and Understanding

Hongliang Li, Lu Fang, Tianzhu Zhang
2020 IEEE transactions on circuits and systems for video technology (Print)  
"Video dialog via multi-grained convolutional self-attention context multi-modal networks," by Gu et al., employs a multigrained convolutional self-attention context network to learn the joint representations  ...  In the deep learning era, metrics and representation learning are achieved by network architecture and loss function design.  ... 
doi:10.1109/tcsvt.2020.3031416 fatcat:gpwbmydqbza5lddatxcfcidwcq

Guest Editorial: Unconstrained Ear Recognition

2018 IET Biometrics  
The research focus in this area seems to move more towards deep learning, which is evidenced by multiple Special Issue papers that deal with deep learning models and techniques built around convolutional  ...  The fourth paper by Dodge, Mounsef and Karan entitled Unconstrained Ear Recognition Using Deep Neural Networks again centers around deep learning and CNNs.  ... 
doi:10.1049/iet-bmt.2018.0011 fatcat:pk5aq6fhtzaejgm46mnrjn2hka

A Comprehensive Overview of Biometric Fusion [article]

Maneet Singh, Richa Singh, Arun Ross
2019 arXiv   pre-print
The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability.  ...  This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse.  ...  Ross was supported by the US National Science Foundation under Grant Numbers 1618518 and 1617466 during the writing of this article.  ... 
arXiv:1902.02919v1 fatcat:4ujumax47vc5hgbddi666fkhda

Deep Hashing for Secure Multimodal Biometrics

Veeru Talreja, Matthew Valenti, Nasser Nasrabadi
2020 IEEE Transactions on Information Forensics and Security  
In this paper, we present a deep learning framework for feature-level fusion that generates a secure multimodal template from each user's face and iris biometrics.  ...  We integrate a deep hashing (binarization) technique into the fusion architecture to generate a robust binary multimodal shared latent representation.  ...  ACKNOWLEDGMENT This research was funded by the Center for Identification Technology Research (CITeR), a National Science Foundation (NSF) Industry/University Cooperative Res. Center (I/UCRC).  ... 
doi:10.1109/tifs.2020.3033189 fatcat:gowcmokitvdarnj7s4qwgbmoqe

Exploring Deep Learning for Joint Audio-Visual Lip Biometrics [article]

Meng Liu, Longbiao Wang, Kong Aik Lee, Hanyi Zhang, Chang Zeng, Jianwu Dang
2021 arXiv   pre-print
Previous works have demonstrated the usefulness of AV lip biometrics. However, the lack of a sizeable AV database hinders the exploration of deep-learning-based audio-visual lip biometrics.  ...  fusion module.  ...  Deep learning has outperformed traditional machine learning methods in most tasks. However, there is no deep-learning based AV lip biometrics, mainly because of the dataset constraint.  ... 
arXiv:2104.08510v1 fatcat:gqidpmx2dvcozeqq3ltzp6rpw4

Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition [article]

Veeru Talreja and Nasser M. Nasrabadi and Matthew C. Valenti
2021 arXiv   pre-print
This paper presents a new deep periocular recognition framework called attribute-based deep periocular recognition (ADPR), which predicts soft biometrics and incorporates the prediction into a periocular  ...  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  ...  Recent developments in periocular recognition techniques are more focused on deep learning-based methods [38, 17] .  ... 
arXiv:2111.01325v1 fatcat:yxz33axaezag3otstaxlfykavy

Learning Discriminative Factorized Subspaces with Application to Touchscreen Biometrics

Neeti Pokhriyal, Venu Govindaraju
2020 IEEE Access  
INDEX TERMS Touchscreen biometrics, multi-modal biometrics, multi-modal data, feature fusion. 152500 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Information fusion is a challenging problem in biometrics, where data comes from multiple biometric modalities or multiple feature spaces extracted from the same modality.  ...  RELATED WORKS IN BIOMETRICS In biometrics, multi-view learning usually has the flavor of multi-modal learning.  ... 
doi:10.1109/access.2020.3014188 fatcat:y7hftyjlmne5th6hrqocvoewfy

Quality-Aware Multimodal Biometric Recognition [article]

Sobhan Soleymani, Ali Dabouei, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Jeremy Dawson, Nasser M. Nasrabadi
2021 arXiv   pre-print
by extracting complimentary identification information based on the quality of the samples.  ...  This framework utilizes two fusion blocks, each represented by a set of quality-aware and aggregation networks.  ...  Multimodal separability and network compactness Inspired by the recent advances in metric learning for deep biometric recognition such as SphereFace [49] , ArcFace [50] , and UniformFace [51] , we design  ... 
arXiv:2112.05827v1 fatcat:nv2we6t6bvbtlf2zzpxd6necf4

Face and Fingerprint Fusion System for Identity Authentication Using Fusion Classifiers

Somashekhar B M, Nijagunarya Y.S
2018 International Journal of Computer Science & Engineering Survey  
Experiments are conducted on Face database and fingerprint database to assess the actualadvantage of the fusion of these biometric traits, in comparison to the unimodal biometric system.  ...  In this work, we propose a feature level fusion and decision level fusion of face and fingerprint for designing a multimodal biometric system.  ...  The researchersconsidered nine biometric spoofing benchmarks and learn deep representations for each benchmark bycombining and contrasting the two learning approaches [10] .  ... 
doi:10.5121/ijcses.2018.9301 fatcat:mtjkpcxk2bfnngu2ws6yncsc3a

Emerging Biometrics: Deep Inference and Other Computational Intelligence [article]

Svetlana Yanushkevich, Shawn Eastwood, Kenneth Lai, Vlad Shmerko
2020 arXiv   pre-print
Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, - the front tides of the modern computational intelligence domain.  ...  Therefore, we focus on intelligent inference engines widely deployed in biometrics.  ...  Acknowledgment This project was partially supported by Natural Sciences and Engineering Research Council of Canada (NSERC) through the grant "Biometric intelligent interfaces".  ... 
arXiv:2006.11971v1 fatcat:k6aunuoxc5apbb347vy2lj7bsq

Noise-tolerant Audio-visual Online Person Verification using an Attention-based Neural Network Fusion [article]

Suwon Shon, Tae-Hyun Oh, James Glass
2018 arXiv   pre-print
Inspired by neuroscientific findings on the association of voice and face, we propose an attention-based end-to-end neural network that learns multi-sensory associations for the task of person verification  ...  The attention mechanism in our proposed network learns to conditionally select a salient modality between speech and facial representations that provides a balance between complementary inputs.  ...  We train the attention networks by the contrastive loss on the joint embedding z ∈ R 600 .  ... 
arXiv:1811.10813v1 fatcat:lk36snzkuffwzll34upoavzua4

Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

Hareesh Mandalapu, Aravinda Reddy P N, Raghavendra Ramachandra, Krothapalli Sreenivasa Rao, Pabitra Mitra, S. R. Mahadeva Prasanna, Christoph Busch
2021 IEEE Access  
Further, a detailed discussion on challenges and open problems is presented in this field of biometrics. INDEX TERMS Biometrics, audio-visual person recognition, presentation attack detection.  ...  Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance.  ...  [63] , which is used in developing a deep learning-based feature fusion.  ... 
doi:10.1109/access.2021.3063031 fatcat:q6emam55frhlzp53t7lxb4qz3e
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