Multiple Face Recognition Using Local Features and Swarm Intelligence

Chidambaram CHIDAMBARAM; Hugo VIEIRA NETO; Leyza Elmeri Baldo DORINI; Heitor Silvério LOPES

doi:10.1587/transinf.E97.D.1614

Multiple Face Recognition Using Local Features and Swarm Intelligence

Chidambaram CHIDAMBARAM, Hugo VIEIRA NETO, Leyza Elmeri Baldo DORINI, Heitor Silvério LOPES

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Face recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images - more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.6 pp.1614-1623

Publication Date: 2014/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.1614

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Chidambaram CHIDAMBARAM
  Santa Catarina State University (UDESC), Joinville
Hugo VIEIRA NETO
  Federal University of Technology - Paran'a (UTFPR), Curitiba
Leyza Elmeri Baldo DORINI
  Federal University of Technology - Paran'a (UTFPR), Curitiba
Heitor Silvério LOPES
  Federal University of Technology - Paran'a (UTFPR), Curitiba

Keyword

local features, iterative search, face recognition

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Chidambaram CHIDAMBARAM, Hugo VIEIRA NETO, Leyza Elmeri Baldo DORINI, Heitor Silvério LOPES, "Multiple Face Recognition Using Local Features and Swarm Intelligence" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 6, pp. 1614-1623, June 2014, doi: 10.1587/transinf.E97.D.1614.
Abstract: Face recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images - more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1614/_p

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@ARTICLE{e97-d_6_1614,
author={Chidambaram CHIDAMBARAM, Hugo VIEIRA NETO, Leyza Elmeri Baldo DORINI, Heitor Silvério LOPES, },
journal={IEICE TRANSACTIONS on Information},
title={Multiple Face Recognition Using Local Features and Swarm Intelligence},
year={2014},
volume={E97-D},
number={6},
pages={1614-1623},
abstract={Face recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images - more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.},
keywords={},
doi={10.1587/transinf.E97.D.1614},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Multiple Face Recognition Using Local Features and Swarm Intelligence
T2 - IEICE TRANSACTIONS on Information
SP - 1614
EP - 1623
AU - Chidambaram CHIDAMBARAM
AU - Hugo VIEIRA NETO
AU - Leyza Elmeri Baldo DORINI
AU - Heitor Silvério LOPES
PY - 2014
DO - 10.1587/transinf.E97.D.1614
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2014
AB - Face recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images - more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.
ER -