A New Approach for Face Recognition Performance Evaluation for Robot Using LED Monitor

Article Preview

Abstract:

With the increasing of service robots, human-robot interaction for natural communication between user and robot is becoming more and more important. Especially, face recognition is a key issue of HRI. Even though robots mainly use face detection and recognition to provide various services, it is still difficult to guarantee of performance due to insufficient test methods in point of view robot. So, we propose a new performance evaluation method for robot using LED monitor.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

939-942

Citation:

Online since:

April 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] TTAK. KO-10. 0418, Performance Evaluation Method of Face Extraction and Identification Algorithm for Intelligent Robots: Part 1 Performance Evaluation of Recognition Algorithm, (2010).

Google Scholar

[2] TTAK. KO-10. 0419, Performance Evaluation Method of Face Extraction and Identification Algorithm for Intelligent Robots: Part 2. System Level Performance Evaluation using Human Model (mannequin) of Human Face Recognition, (2010).

Google Scholar

[3] TTAK. KO-10. 0507, Performance Evaluation Method of Face Extraction and Identification Algorithm for Intelligent Robots: Part 3. Performance Evaluation of Face Recognition using Face Photos, (2011).

Google Scholar

[4] http: /www. cie. co. at.

Google Scholar

[5] M.Y. Cho, Y.S. Jeong, B.T. Chun, A Study on Face Recognition Performance Comparison of Real Images with Images from LED Monitor, Journal of the Institute of Electronics Engineers of Korea, Vol. 50, No. 5, p.1164~1169, May (2013).

DOI: 10.5573/ieek.2013.50.5.144

Google Scholar

[6] P. J. Phillips, H. Moon, P. J. Rauss, and S. Rizvi. The feret evaluation methodology for face recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10), (2000).

DOI: 10.1109/34.879790

Google Scholar

[7] Hyoung-Soo Lee, Sungsoo Park, Bong-Nam Kang, Jongju Shin, Ju-Young Lee, Hongmo Je, Bongjin Jun, Daijin Kim, The POSTECH Face Database (PF07) and Performance Evaluation, in Proc. IEEE Int. Conf. Automatic Face & Gesture Recognition, Sep. 2008, p.1.

DOI: 10.1109/afgr.2008.4813378

Google Scholar

[8] T. Sim, S. Baker, and M. Bsat. The CMU pose, illumination, and expression database, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), (2003).

DOI: 10.1109/tpami.2003.1251154

Google Scholar

[9] KS A 3011 Recommended levels of illumination.

Google Scholar