loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Chenqi Wang ; Kevin Lin and Yi-Ping Hung

Affiliation: National Taiwan University, Taiwan

Keyword(s): Face Recognition, Local Binary Pattern (LBP), Unsupervised Learning.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis

Abstract: In this paper, we present a mechanism to extract certain special faces—LBP-Faces, which are designed to represent different kinds of faces around the world, and utilize them as the basis to verify other faces. In particular, we show how our idea can integrate with Local Binary Pattern (LBP) and improve its performance. Other than most of the previous LBP-variant approaches, which, no matter try to improve coding mechanism or optimize the neighbourhood sizes, first divide a face into patch-level regions (e.g. 7×7 patches), concatenating histograms calculated in each patch to derive a rather long dimension vector, and then apply PCA to implement dimension reduction, our work use original LBP histograms, trying to retain the major properties such as discriminability and invariance, but in a much bigger component-level region (we divide faces into 7 components). In each component, we cluster LBP descriptors—in the form of histograms to derive N clustering centroids, which we define as LB P-Faces. Then, to any input face, we calculate its similarities with all these N LBP-Faces and use the similarities as final features to verify the face. It looks like we project the faces image into a new feature space—LBP-Faces space. The intuition within it is that when we depict an unknown face, we are prone to use description such as how likely the face’s eye or nose is to an known one. Result of our experiment on the Labeled Face in Wild (LFW) database shows that our method outperforms LBP in face verification. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.239.185.22

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Wang, C.; Lin, K. and Hung, Y. (2014). Face Verification using LBP Feature and Clustering. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP; ISBN 978-989-758-003-1; ISSN 2184-4321, SciTePress, pages 572-578. DOI: 10.5220/0004736905720578

@conference{visapp14,
author={Chenqi Wang. and Kevin Lin. and Yi{-}Ping Hung.},
title={Face Verification using LBP Feature and Clustering},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP},
year={2014},
pages={572-578},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004736905720578},
isbn={978-989-758-003-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP
TI - Face Verification using LBP Feature and Clustering
SN - 978-989-758-003-1
IS - 2184-4321
AU - Wang, C.
AU - Lin, K.
AU - Hung, Y.
PY - 2014
SP - 572
EP - 578
DO - 10.5220/0004736905720578
PB - SciTePress