Illumination invariant efficient face recognition using a single training image

Bharat Jangid; K.K. Biswas; M. Hanmandlu; Girija CHETTY

doi:10.1109/DICTA.2015.7371266

Illumination invariant efficient face recognition using a single training image

Bharat Jangid, K.K. Biswas, M. Hanmandlu, Girija CHETTY

Information, Technology (IT) & Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper presents a single sample face recognition technique which takes care of illumination variations by applying normalization based on Weber's law. Local Directional Pattern (LDP) features are extracted from the normalized face by examining the prominent edge directions at each pixel. The LDP image is divided into non-overlapping windows and each window is treated as a fuzzy set. Treating LDP values as the information source values, entropy features called the information set- based features are extracted from each window. Further, 2DPCA is used to reduce the number of features. These features are augmented with entropy features of the fiducial regions and contour based features for face recognition. A nearest neighbor classifier based on these features is used on Extended Yale B and Face94 datasets and it is shown that compared with other results based on single and multiple training images, the proposed approach results in better recognition accuracy for wide illumination variations in test images. Further the efficiency of the scheme is shown by comparing the number of features needed for recognition.

Original language	English
Title of host publication	Proceedings International Conference on Digital Image Computing and Applications 2015
Subtitle of host publication	Techniques and Applications, DICTA 2015
Editors	Jamie Sherrah, David Suter
Place of Publication	Adelaide, Australia
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-7
Number of pages	7
Volume	1
ISBN (Electronic)	9781467367943
ISBN (Print)	9781467367950
DOIs	https://doi.org/10.1109/DICTA.2015.7371266
Publication status	Published - 23 Nov 2015
Event	2015 Conference on Digital Image Computing, Techniques and Applications - Adelaide, Adelaide, Australia Duration: 23 Nov 2015 → 25 Nov 2015

Publication series

Name	2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015

Conference

Conference	2015 Conference on Digital Image Computing, Techniques and Applications
Abbreviated title	DICTA 2015
Country	Australia
City	Adelaide
Period	23/11/15 → 25/11/15
Other	The International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS). DICTA 2015 is endorsed by the IAPR and technically co-sponsored by the IEEE

Access to Document

10.1109/DICTA.2015.7371266

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Illumination invariant efficient face recognition using a single training image'. Together they form a unique fingerprint.

Cite this

Jangid, B., Biswas, K. K., Hanmandlu, M., & CHETTY, G. (2015). Illumination invariant efficient face recognition using a single training image. In J. Sherrah, & D. Suter (Eds.), Proceedings International Conference on Digital Image Computing and Applications 2015: Techniques and Applications, DICTA 2015 (Vol. 1, pp. 1-7). [7371266] (2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2015.7371266

Jangid, Bharat ; Biswas, K.K. ; Hanmandlu, M. ; CHETTY, Girija. / Illumination invariant efficient face recognition using a single training image. Proceedings International Conference on Digital Image Computing and Applications 2015: Techniques and Applications, DICTA 2015. editor / Jamie Sherrah ; David Suter. Vol. 1 Adelaide, Australia : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 1-7 (2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015).

@inproceedings{f0a39a2906e646648b9457d0dd4cbcea,

title = "Illumination invariant efficient face recognition using a single training image",

abstract = "This paper presents a single sample face recognition technique which takes care of illumination variations by applying normalization based on Weber's law. Local Directional Pattern (LDP) features are extracted from the normalized face by examining the prominent edge directions at each pixel. The LDP image is divided into non-overlapping windows and each window is treated as a fuzzy set. Treating LDP values as the information source values, entropy features called the information set- based features are extracted from each window. Further, 2DPCA is used to reduce the number of features. These features are augmented with entropy features of the fiducial regions and contour based features for face recognition. A nearest neighbor classifier based on these features is used on Extended Yale B and Face94 datasets and it is shown that compared with other results based on single and multiple training images, the proposed approach results in better recognition accuracy for wide illumination variations in test images. Further the efficiency of the scheme is shown by comparing the number of features needed for recognition.",

keywords = "2DPCA, Entropy features, Entropy function, Extended Yale B dataset, Illumination invariant, Information set, Local directional pattern (LDP)",

author = "Bharat Jangid and K.K. Biswas and M. Hanmandlu and Girija CHETTY",

year = "2015",

month = nov,

day = "23",

doi = "10.1109/DICTA.2015.7371266",

language = "English",

isbn = "9781467367950",

volume = "1",

series = "2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--7",

editor = "Jamie Sherrah and David Suter",

booktitle = "Proceedings International Conference on Digital Image Computing and Applications 2015",

address = "United States",

note = "2015 Conference on Digital Image Computing, Techniques and Applications, DICTA 2015 ; Conference date: 23-11-2015 Through 25-11-2015",

}

Jangid, B, Biswas, KK, Hanmandlu, M & CHETTY, G 2015, Illumination invariant efficient face recognition using a single training image. in J Sherrah & D Suter (eds), Proceedings International Conference on Digital Image Computing and Applications 2015: Techniques and Applications, DICTA 2015. vol. 1, 7371266, 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, IEEE, Institute of Electrical and Electronics Engineers, Adelaide, Australia, pp. 1-7, 2015 Conference on Digital Image Computing, Techniques and Applications, Adelaide, Australia, 23/11/15. https://doi.org/10.1109/DICTA.2015.7371266

Illumination invariant efficient face recognition using a single training image. / Jangid, Bharat; Biswas, K.K.; Hanmandlu, M.; CHETTY, Girija.

Proceedings International Conference on Digital Image Computing and Applications 2015: Techniques and Applications, DICTA 2015. ed. / Jamie Sherrah; David Suter. Vol. 1 Adelaide, Australia : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 1-7 7371266 (2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Illumination invariant efficient face recognition using a single training image

AU - Jangid, Bharat

AU - Biswas, K.K.

AU - Hanmandlu, M.

AU - CHETTY, Girija

PY - 2015/11/23

Y1 - 2015/11/23

N2 - This paper presents a single sample face recognition technique which takes care of illumination variations by applying normalization based on Weber's law. Local Directional Pattern (LDP) features are extracted from the normalized face by examining the prominent edge directions at each pixel. The LDP image is divided into non-overlapping windows and each window is treated as a fuzzy set. Treating LDP values as the information source values, entropy features called the information set- based features are extracted from each window. Further, 2DPCA is used to reduce the number of features. These features are augmented with entropy features of the fiducial regions and contour based features for face recognition. A nearest neighbor classifier based on these features is used on Extended Yale B and Face94 datasets and it is shown that compared with other results based on single and multiple training images, the proposed approach results in better recognition accuracy for wide illumination variations in test images. Further the efficiency of the scheme is shown by comparing the number of features needed for recognition.

AB - This paper presents a single sample face recognition technique which takes care of illumination variations by applying normalization based on Weber's law. Local Directional Pattern (LDP) features are extracted from the normalized face by examining the prominent edge directions at each pixel. The LDP image is divided into non-overlapping windows and each window is treated as a fuzzy set. Treating LDP values as the information source values, entropy features called the information set- based features are extracted from each window. Further, 2DPCA is used to reduce the number of features. These features are augmented with entropy features of the fiducial regions and contour based features for face recognition. A nearest neighbor classifier based on these features is used on Extended Yale B and Face94 datasets and it is shown that compared with other results based on single and multiple training images, the proposed approach results in better recognition accuracy for wide illumination variations in test images. Further the efficiency of the scheme is shown by comparing the number of features needed for recognition.

KW - 2DPCA

KW - Entropy features

KW - Entropy function

KW - Extended Yale B dataset

KW - Illumination invariant

KW - Information set

KW - Local directional pattern (LDP)

UR - http://www.scopus.com/inward/record.url?scp=84963705362&partnerID=8YFLogxK

U2 - 10.1109/DICTA.2015.7371266

DO - 10.1109/DICTA.2015.7371266

M3 - Conference contribution

SN - 9781467367950

VL - 1

T3 - 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015

SP - 1

EP - 7

BT - Proceedings International Conference on Digital Image Computing and Applications 2015

A2 - Sherrah, Jamie

A2 - Suter, David

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Adelaide, Australia

T2 - 2015 Conference on Digital Image Computing, Techniques and Applications

Y2 - 23 November 2015 through 25 November 2015

ER -

Jangid B, Biswas KK, Hanmandlu M, CHETTY G. Illumination invariant efficient face recognition using a single training image. In Sherrah J, Suter D, editors, Proceedings International Conference on Digital Image Computing and Applications 2015: Techniques and Applications, DICTA 2015. Vol. 1. Adelaide, Australia: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 1-7. 7371266. (2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015). https://doi.org/10.1109/DICTA.2015.7371266