Human Face Recognition Based on the Core Characteristics under the Condition of Illumination

Wen Hui Li; Ning Ma; Zhi Yan Wang

doi:10.4028/www.scientific.net/AMM.484-485.991

Paper Titles

Video Segmentation Based on Area Selection
p.972

Blind Estimation of Long Code DSSS Signal Based on Subspace Tracking
p.976

Optimal Path Algorithm Analysis of GPS Navigation Based on A* Algorithm
p.982

An Indirect Fingerprint Authentication Scheme in Cloud Computing
p.986

Human Face Recognition Based on the Core Characteristics under the Condition of Illumination
p.991

Study on Factors of College Student's Network Service Time Based on Bayesian Networks
p.996

Research on Recognition Method of Handwritten Numerals Segmentation Based on B-P Neural Network
p.1001

Study on Component Composition Frame Based on XCM Model
p.1006

Key Factors of Agricultural Industry Evaluation Based on AHP and TFN
p.1012

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 484-485Human Face Recognition Based on the Core...

Human Face Recognition Based on the Core Characteristics under the Condition of Illumination

Abstract:

A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of light. With this method, if human face image is affected by light and the illumination is forward or side can be judged; the images affect by illumination are processed using the strategy of frequency domain replacement, and then the key areas of human face image are divided and then are recognized using support vector machine (SVM) based on the unit of area, and finally the recognition results are integrated. The experimental result shows that this method can produce a better recognition effect than other methods in view of the problem of illumination.

You might also be interested in these eBooks

Green Power, Materials and Manufacturing Technology and Applications III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 484-485)

Pages:

991-995

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.484-485.991

Citation:

Cite this paper

Online since:

January 2014

Authors:

Wen Hui Li*, Ning Ma, Zhi Yan Wang

Keywords:

Division of Areas, Effect of Illumination, Frequency Domain Replacement, Human Face Recognition, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] L Y Qing, S G Shan, W Gao. Face Recognition under Generic Illumination Based on Harmonic Reilluminationing . International Journal of Pattern Recognition and Artificial Intelligence, 2005, 19 (4): 513~531.

DOI: 10.1142/s0218001405004186

Google Scholar

[2] X J Chai, L Y Qing, S G Shan. Pose Invariant Face Recognition under Arbitrary Illumination based on 3D Face Reconstruction. Audio and Video based Biometric Person Authentication, 2005: 956~965.

DOI: 10.1007/11527923_100

Google Scholar

[3] Chen H F, Belhumeur P N, Jacobs D W. In Search of Illumination Invariants. In: Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA., 2000: 254-261.

DOI: 10.1109/cvpr.2000.855827

Google Scholar

[4] Adini Y, Moses Y, Ullman s. Face Recognition: The Problem of Compensating for Changes in Illumination Direction. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 721~732.

DOI: 10.1109/34.598229

Google Scholar

[5] Belhumeur P N, Kriegman D J. What is the Set of Images of an Object under All Possible Illuminationing Conditions? International Journal of Computer Vision, 1998, 28(3): 1~16.

DOI: 10.1109/cvpr.1996.517085

Google Scholar

[6] Georghiades A S, Belhumeur P N, Kriegman D J. From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans on Pattern Analysis and Machine Intelligence. 2001, 23 (6): 643~660.

DOI: 10.1109/34.927464

Google Scholar

[7] Wang H T, Li S Z, Wang Y S, Zhang W W. Illumination Modeling and Normalization for Face Recognition. In: Proc of IEEE International Workshop on Analysis and Modeling of Faces and Gestures. Nice, France, 2003, 104~111.

Google Scholar

[8] Yuan Xun, Wu Xiuqing, Hong Richang, et al. Video Classification based on Active Learning SVM Separator . Journal of University of Science and Technology of China, 2009, 39(5): 473~478.

Google Scholar

[9] Guosheng Wang. Study on the Theory and Algorithm of Support Vector Machine (SVM) . Beijing: Beijing University of Posts and Telecommunications, 2008 (in Chinese).

Google Scholar

[10] Saiqin Xie, Shen Fuming, Qiu Xuena. The Face Recognition Method based on Support Vector Machine . Computer Engineering, 2009, 35 (16): 186~188.

Google Scholar

[11] Congdong Xu, Chun Chen, Anguo Zhu. Skin Color Detection based on Support Vector Machine (SVM) and Neighborhood Information . Computer Applications and Software, 2009, 26 (5): 202~204.

Google Scholar