Paper Info
Title
Facial landmarking: comparing automatic landmarking methods with applications in soft biometrics
Abstract
Registration is a critical step in computer-based image analysis. In this work we examine the effects of registration in face-based soft-biometrics. This form of soft-biometrics, better termed as facial analytics, takes an image containing a face and returns attributes of that face. In this work, the attributes of focus are gender and race. Automatic generation of facial analytics relies on accurate registration. Hence, this work evaluates three techniques for dense registration, namely AAM, Stacked ASM and CLM. Further, we evaluate the influence of facial landmark mis-localization, resulting from these techniques, on gender classification and race determination. To the best of our knowledge, such an evaluation of landmark mis-localization on soft biometrics, has not been conducted. We further demonstrate an effective system for gender and race classification based on dense landmarking and multi-factored principle components analysis. The system performs well against a multi-age face dataset for both gender and race classification.
Year
DOI
Venue
2012
10.1007/978-3-642-33868-7_29
ECCV Workshops
Keywords
Field
DocType
automatic landmarking method,facial landmark mis-localization,computer-based image analysis,soft biometrics,race determination,accurate registration,dense landmarking,race classification,multi-age face dataset,facial landmarking,gender classification,dense registration,facial analytics
Computer vision,Soft biometrics,Pattern recognition,Computer science,Artificial intelligence,Landmark,Analytics,Principal component analysis
Conference
Volume
ISSN
Citations 
7584
0302-9743
2
PageRank 
References 
Authors
0.43
8
4
Name
Order
Citations
PageRank
Amrutha Sethuram1173.41
Karl Ricanek216518.65
Jason M. Saragih3166869.02
Chris Boehnen4955.99