NDLI: Robust Face Recognition via Multimodal Deep Face Representation

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Table of Contents

IEEE Transactions on Multimedia publication information

Guest Editorial: Deep Learning for Multimedia Computing

Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks

Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition

RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge

A Continuous Learning Framework for Activity Recognition Using Deep Hybrid Feature Models

Unconstrained Multimodal Multi-Label Learning

Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO

Multi-Task CNN Model for Attribute Prediction

Learning Representative Deep Features for Image Set Analysis

Cross Indexing With Grouplets

Understanding Blooming Human Groups in Social Networks

Weakly Supervised Deep Metric Learning for Community-Contributed Image Retrieval

Learning Cross Space Mapping via DNN Using Large Scale Click-Through Logs

Deep Multimodal Learning for Affective Analysis and Retrieval

Rating Image Aesthetics Using Deep Learning

Query-Dependent Aesthetic Model With Deep Learning for Photo Quality Assessment

Robust Face Recognition via Multimodal Deep Face Representation

Deep Learning and Music Adversaries

DeepBag: Recognizing Handbag Models

Predicting Eye Fixations on Webpage With an Ensemble of Early Features and High-Level Representations from Deep Network

Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

Call for papers: Special issue on multimedia-based healthcare

IEEE International Conference of Multimedia and Expo (ICME) 2016

Introducing IEEE Collabratec

IEEE Transactions on Multimedia information for authors

IEEE Transactions on Multimedia society information

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Robust Face Recognition via Multimodal Deep Face Representation

Content Provider	IEEE Xplore Digital Library
Author	Changxing Ding Dacheng Tao
Copyright Year	1999
Abstract	Face images appearing in multimedia applications, e.g., social networks and digital entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in considerable performance degradation for traditional face recognition algorithms. This paper proposes a comprehensive deep learning framework to jointly learn face representation using multimodal information. The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE). The set of CNNs extracts complementary facial features from multimodal data. Then, the extracted features are concatenated to form a high-dimensional feature vector, whose dimension is compressed by SAE. All of the CNNs are trained using a subset of 9,000 subjects from the publicly available CASIA-WebFace database, which ensures the reproducibility of this work. Using the proposed single CNN architecture and limited training data, 98.43% verification rate is achieved on the LFW database. Benefitting from the complementary information contained in multimodal data, our small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.
Sponsorship	IEEE Signal Processing Society IEEE Circuits and Systems Society IEEE Communications Society IEEE Computer Society
Starting Page	2049
Ending Page	2058
Page Count	10
File Size	1707393
File Format	PDF
ISSN	15209210
Volume Number	17
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Feature extraction Face recognition Databases Training Multimedia communication Social network services multimodal system Convolutional neural networks (CNNs) deep learning face recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Science Applications Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Patch strategy for deep face recognition

Understanding Blooming Human Groups in Social Networks

Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO

Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy

Image classification based on convolutional neural networks with cross-level strategy

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

Improved deep face identification with multi-class pairwise discriminant loss

Bi-directional CRC algorithm using CNN-based features for face classification

Mouse face tracking using convolutional neural networks

Robust Face Recognition via Multimodal Deep Face Representation

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