Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Face recognition method using embedded data in Principal Component Analysis

주성분분석 방법에서의 임베디드 데이터를 이용한 얼굴인식 방법

  • Park Chang-Han (Department of Computer Engineering, Kwangwoon University) ;
  • Namkung Jae-Chan (Department of Computer Engineering, Kwangwoon University)
  • Published : 2005.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose face recognition method using embedded data in super states segmentalized that is specification region exist to face region, hair, forehead, eyes, ears, nose, mouth, and chin. Proposed method defines super states that is specification area in normalized size (92×112), and embedded data that is extract internal factor in super states segmentalized achieve face recognition by PCA algorithm. Proposed method can receive specification data that is less in proposed image's size (92×112) because do orignal image to learn embedded data not to do all loaming. And Showed face recognition rate in image of 92×112 size averagely 99.05%, step 1 99.05%, step 2 98.93%, step 3 98.54%, step 4 97.85%. Therefore, method that is proposed through an experiment showed that the processing speed improves as well as reduce existing face image's information.

본 논문에서는 얼굴영역에 존재하는 특정영역인 분할된 머리, 이마, 눈, 귀, 코, 입, 턱의 슈퍼 상태에서 임베디드 데이터를 이용하여 얼굴인식 방법을 제안한다. 제안된 방법에서는 정규화된 크기(92×112)에서 특정영역인 슈퍼 상태를 정의하고, 분할된 슈퍼 상태의 내부요소인 임베디드 데이터만을 추출하여 PCA 알고리듬으로 얼굴인식을 수행한다. 제안된 방법에서는 원래영상을 모두 학습하는 것이 아니라 분할된 임베디드 데이터만을 학습시키기 때문에 제안된 영상의 크기(92×112)에서 특정 데이터를 받아들일 수 있다. 그리고 평균적으로 92×112 크기의 영상에서는 99.05%, 단계1은 99.05%, 단계2는 98.93%, 단계3은 98.54%, 단계4는 97.85%의 얼굴인식률을 보였다. 따라서 실험을 통하여 제안된 방법은 얼굴영상의 정보를 축소할 뿐만 아니라 처리속도도 향상됨을 보였다.

Keywords

References

  1. M.-H. Yang, D. J. Kriegman, and N. Ahuja, 'Detecting faces in images: A survey,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002 https://doi.org/10.1109/34.982883
  2. Z. Sun, G. Bebis, X. Yuan, S. J. Louis, 'Genetic Feature Subset Selection for Gender Classification: A Comparison Study', Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on, 2002. pp.165-170. Dec 2002 https://doi.org/10.1109/ACV.2002.1182176
  3. F. Samaria and S. Young, 'HMM based architecture for face identification', Image and Vision Computing, vol. 12, pp. 537-543, 1994 https://doi.org/10.1016/0262-8856(94)90007-8
  4. Y. Hongxun, L. Mingbao, Z. Lizhuang, 'Eigen features technique and its application', Signal Processing Proceedings, 2000. WCCC-ICSP 2000, vol. 2, pp. 1153-1158, 21-25 Aug. 2000 https://doi.org/10.1109/ICOSP.2000.891747
  5. B.A. McLindin, 'Baselining illumination variables for improved facial recognition system performance', Video/Image Processing and Multimedia Communications, 2003. 4th EURASIP Conference, Vol. 1, pp. 417-422, 2-5 July 2003 https://doi.org/10.1109/VIPMC.2003.1220497
  6. Y. Zhong, J. A.K., D. Jolly, M. -P, 'Object tracking using deformable templates', Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol. 22, no. 5, pp. 544-549, May. 2000 https://doi.org/10.1109/34.857008
  7. T. Liang, H. K. Kwan, 'Automatic localization of human eyes in complex background', Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium, vol. 5, pp. 669-672, 26-29 May 2002 https://doi.org/10.1109/ISCAS.2002.1010792
  8. L.Chengjun, H.Wechsler, 'Independent component analysis of Gabor features for face recognition', Neural Networks, IEEE Transactions. vol. 14, no. 4, pp. 919-928, July 2003 https://doi.org/10.1109/TNN.2003.813829
  9. R.S. Feris, R.M. Cesar, 'Tracking Facial Features Using Gabor Wavelet Networks', Proc. 13th Brazilian Symposium on Computer Graphics and Image Processing, pp. 22-27, 2000 https://doi.org/10.1109/SIBGRA.2000.883889
  10. T. Kawaguchi, D. Hidaka, M. Rizon, 'Detection of eyes from human faces by Hough transform and separability filter', Image Processing, 2000. Proceedings. 2000 International Conference on, vol. 1, pp. 49-52, 10-13 Sept. 2000 https://doi.org/10.1109/ICIP.2000.900889
  11. M. Rizon, T. Kawaguchi, 'Automatic eye detection using intensity and edge information', TENCON 2000. Proceedings, vol. 2, pp. 415-420, 24-27 Sept. 2000 https://doi.org/10.1109/TENCON.2000.888773
  12. http://www.uk.research.att.com/facedatabase.html
  13. L. Zhi-fang, Y. Zhi-sheng, A. K. Jain, W. Yunqiong, 'Face detection and facial feature extraction in color image', Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference, pp. 126-130, 27-30 Sept. 2003
  14. H. Rowley, S. Baluja and T. Kanade, 'Neural Network-Based Face Detection', IEEE Trans. Patt. Anal. Machine Intell., vol. 20, no. 1, pp. 203-208, 1998 https://doi.org/10.1109/34.655647