NDLI: Three-Band Modeling Using Prominent Edges for Face Alignment

Content Provider	IEEE Xplore Digital Library
Author	Kahraman, F. Kurt, B. Gokmen, M.
Copyright Year	2006
Description	Author affiliation: Bilisim Enstitusu, Istanbul Tech. Univ. (Kahraman, F.)
Abstract	Fundamental difficulty in face recognition systems is mainly related to successful human face alignment from the input image. In recent years, model based approaches get attention among others. Most powerful method among model-based approaches is known as active appearance model. The method accomplishes this by constructing a relation between shape and texture. Face alignment methods are required to work well even in the presence of illumination and affine transformation. Classical AAM extracts texture and shape information from the training image by using RGB color space. Classical AAM can only handle images having the same or similar color distribution to the images in the training set. Classical AAM cannot align images obtained under different lightning conditions from the training images even if the same person exists in the training database. In this study, we propose to use features which are shown to be less sensitive to illumination changes instead of directly using RGB colors. The proposed AAM is called three-band AAM. The bands are hue, hill, and luminance. Prominent edge detection constitutes the most important part of the model. Experimental studies show that prominent edges do not depend on illumination changes much when compared the original color space, and the three-band AAM based face alignment outperforms the classical AAM in terms of alignment precision
Starting Page	1
Ending Page	4
File Size	4944532
Page Count	4
File Format	PDF
ISBN	1424402387
DOI	10.1109/SIU.2006.1659716
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-17
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Image databases Image edge detection Face recognition Lighting Humans Lightning Active appearance model Data mining Power system modeling
Content Type	Text
Resource Type	Article

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

An Active Illumination and Appearance (AIA) Model for Face Alignment

Multi-view face alignment using direct appearance models

Face detection using gradient vector flow

Active appearance model based face recognition

Face reconstruction and recognition using a statistical model combining shape and spherical harmonics

Illumination Invariant Three-Stage Approach for Face Alignment

A Statistical Model Combining Shape and Spherical Harmonics for Face Reconstruction and Recognition

Real-time view-based face alignment using active wavelet networks

Automatic Facial Expression Recognition Using both Local and Global Information

Three-Band Modeling Using Prominent Edges for Face Alignment

Similar Documents

An Active Illumination and Appearance (AIA) Model for Face Alignment

Multi-view face alignment using direct appearance models

Face detection using gradient vector flow

Active appearance model based face recognition

Face reconstruction and recognition using a statistical model combining shape and spherical harmonics

Illumination Invariant Three-Stage Approach for Face Alignment

A Statistical Model Combining Shape and Spherical Harmonics for Face Reconstruction and Recognition

Real-time view-based face alignment using active wavelet networks

Automatic Facial Expression Recognition Using both Local and Global Information

Three-Band Modeling Using Prominent Edges for Face Alignment