NDLI: A Deep Sum-Product Architecture for Robust Facial Attributes Analysis

Content Provider	IEEE Xplore Digital Library
Author	Ping Luo Xiaogang Wang Xiaoou Tang
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Ping Luo; Xiaoou Tang) \|\| Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Xiaogang Wang)
Abstract	Recent works have shown that facial attributes are useful in a number of applications such as face recognition and retrieval. However, estimating attributes in images with large variations remains a big challenge. This challenge is addressed in this paper. Unlike existing methods that assume the independence of attributes during their estimation, our approach captures the interdependencies of local regions for each attribute, as well as the high-order correlations between different attributes, which makes it more robust to occlusions and misdetection of face regions. First, we have modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region. The detector allows us to locate the region, while the classifier determines the presence or absence of an attribute. Second, correlations of attributes and attribute predictors are modeled by organizing all of the decision trees into a large sum-product network (SPN), which is learned by the EM algorithm and yields the most probable explanation (MPE) of the facial attributes in terms of the region's localization and classification. Experimental results on a large data set with 22,400 images show the effectiveness of the proposed approach.
Sponsorship	IEEE Comput. Soc.
Starting Page	2864
Ending Page	2871
File Size	1422764
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.356
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Face Training Decision trees Robustness Detectors Joints deep learning face recognition attributes
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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

Hierarchical face parsing via deep learning

A deep sum-product architecture for robust facial attributes analysis

Exemplar-Based Graph Matching for Robust Facial Landmark Localization

Robust Facial Expression Recognition Using Revised Canonical Correlation

Facial Expression Recognition via a Boosted Deep Belief Network

X.: A deep sum-product architecture for robust facial attributes analysis (2013)

Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Cascaded Shape Space Pruning for Robust Facial Landmark Detection

Relative Parts: Distinctive Parts for Learning Relative Attributes

A Deep Sum-Product Architecture for Robust Facial Attributes Analysis

Similar Documents

Hierarchical face parsing via deep learning

A deep sum-product architecture for robust facial attributes analysis

Exemplar-Based Graph Matching for Robust Facial Landmark Localization

Robust Facial Expression Recognition Using Revised Canonical Correlation

Facial Expression Recognition via a Boosted Deep Belief Network

X.: A deep sum-product architecture for robust facial attributes analysis (2013)

Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Cascaded Shape Space Pruning for Robust Facial Landmark Detection

Relative Parts: Distinctive Parts for Learning Relative Attributes

A Deep Sum-Product Architecture for Robust Facial Attributes Analysis