NDLI: Effects of pose and image resolution on automatic face recognition

Content Provider	IET Digital Library
Author	Mahmood, Zahid Ali, Tauseef Khan, Samee U.
Abstract	The popularity of face recognition systems have increased due to their use in widespread applications. Driven by the enormous number of potential application domains, several algorithms have been proposed for face recognition. Face pose and image resolutions are among the two important factors that influence the performance of face recognition algorithms. In this study, the authors present a comparative study of three baseline face recognition algorithms to analyse the effects of two aforementioned factors. The algorithms studied include (a) the adaptive boosting (AdaBoost) with linear discriminant analysis as weak learner, (b) the principal component analysis (PCA)-based approach, and (c) the local binary pattern (LBP)-based approach. They perform an empirical study using the images with systematic pose variation and resolution from multi-pose, illumination, and expression database to explore the recognition accuracy. This evaluation is useful for practical applications because most engineers start development of a face recognition application using these baseline algorithms. Simulation results revealed that the PCA is more accurate in classifying the pose variation, whereas the AdaBoost is more robust in identifying low-resolution images. The LBP does not classify face images of size 20 × 20 pixels and below and has lower recognition accuracy than PCA and AdaBoost.
Starting Page	111
Ending Page	119
Page Count	9
ISSN	20474938
Volume Number	5
e-ISSN	20474946
Issue Number	Issue 2, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/5/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2015.0008
Journal	IET Biometrics
Publisher Date	2016-06-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	AdaBoost Adaptive Boosting Automatic Face Recognition Baseline Face Recognition Algorithm Biometric Access Control Biomimetics Computer Vision And Image Processing Technique Face Recognition Face Recognition System Illumination Image Classification Image Recognition Image Resolution Knowledge Engineering Technique Law Enforcement LBP-based Approach Learning in AI Linear Discriminant Analysis Local Binary Pattern Low-resolution Image PCA-based Approach Principal Component Analysis Security Statistics Systematic Pose Variation
Content Type	Text
Resource Type	Article
Subject	Signal Processing 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

1 The Effects of Pose and Image Resolution on Automatic Face Recognition

Class-wise two-dimensional PCA method for face recognition

Low-resolution face recognition and the importance of proper alignment

Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular-based tensor locality preservative projections

Label propagation approach for predicting missing biographic labels in face-based biometric records

Pose-based deep gait recognition

Multiview face recognition based on multilinear decomposition and pose manifold

Gentle Adaboost algorithm based on multi-feature fusion for face detection

Robust face anti-spoofing using CNN with LBP and WLD

Effects of pose and image resolution on automatic face recognition

Similar Documents

1 The Effects of Pose and Image Resolution on Automatic Face Recognition

Class-wise two-dimensional PCA method for face recognition

Low-resolution face recognition and the importance of proper alignment

Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular-based tensor locality preservative projections

Label propagation approach for predicting missing biographic labels in face-based biometric records

Pose-based deep gait recognition

Multiview face recognition based on multilinear decomposition and pose manifold

Gentle Adaboost algorithm based on multi-feature fusion for face detection

Robust face anti-spoofing using CNN with LBP and WLD

Effects of pose and image resolution on automatic face recognition