The Journal of Korea Institute of Information, Electronics, and Communication Technology (한국정보전자통신기술학회논문지)

Korea Information Electronic Communication Technology (한국정보전자통신기술학회)
권호 표지
DOI QR코드

DOI QR Code

Face recognition using PCA and face direction information

PCA와 얼굴방향 정보를 이용한 얼굴인식

  • Kim, Seung-Jae (Department of Computer Engineering, Chosun University)
  • Received : 2017.12.09
  • Accepted : 2017.12.26
  • Published : 2017.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

본 논문은 얼굴 인식에 있어 안정적인 인식률을 얻기 위해 입력 영상에 대한 좌우 회전정보를 사용하여 보다 안정적이며 높은 인식률을 내기위한 알고리즘을 제안한다. 제안하는 알고리즘은 웹 카메라 환경에서 얼굴 영상을 입력정보로 사용하여 향상된 인식률을 얻기 위해 영상의 사이즈 축소 및 밝기와 컬러에 대한 정보를 정규화한 후 전처리 과정을 거쳐 얼굴 영역만을 분할 검출한다. 검출된 후보 영역에 대해 주성분분석(PCA)을 적용하여 특징벡터를 구하여 얼굴을 분류한다. 또한 인식률의 오차 범위를 줄이기 위해 입력되는 얼굴 영상에 대한 방향성을 고려하여 좌 우 $45^{\circ}$ 회전 정보를 가진 영상을 대상으로 데이터 셋을 구성하여 PCA로 각각의 특징벡터를 구하였다. 구해진 특징벡터로 안정된 인식률을 얻기 위해 고유공간에 뿌린 후 각각의 특징들을 대상으로 유클리디안(euclidean distant) 거리를 비교하여 최종 얼굴을 인식한다. PCA에 의한 특징벡터는 저차원의 데이터이지만 얼굴을 표현하는데 있어 아무런 문제가 없으며 계산량이 적어 인식 속도도 빠를 수 있다. 본 논문에서 제안하는 방법은 기존의 다른 알고리즘에 비해 빠른 인식과 인식률의 안전성과 정확성을 향상시킬 수 있고 실시간 인식 시스템에도 사용할 수 있다.

Keywords

References

  1. R. C. Gonzalez and R. E. Woods "Digital Image Processing," Prentice Hall, 2002.
  2. K. Yung-Wei, G. Hui-Zhen, Y. Shyan-Ming, "Integration of face and hand gesture recognition", Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third Intermational Conference on, Vol. 1, pp. 330-335, 2008.
  3. J. M. Kim and M. K. Song, "Three Dimensional Gesture Recognition Using PCA of Stereo Images and Modified Matching Algorithm," IEEE Fuzzy Systems and Knowledge Discovery Vol.4, pp.116-120, Oct, 2008
  4. Myung-Hwan Geum, Jung-Soo Kim, Jun-Gyn Lee, and Bong-Hwan Lee, "A User Authentication System using Multi-Modal Biometrics", Korean Information Processing Society 27, Vol. 14, No. 1, 2007(5).
  5. R. O. Duda, P. E. Hart, and D. G. Strok, Pattern Classification, Second Edition by John Wiley & Sons, Inc, 2001.
  6. M. O. Faruqe, M. Al Mehedi Hasan, "Face Recognition Using PCA and SVM", Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on, pp. 97-101, 2009.
  7. Jian Yang, Jing-Ju Yang, "Why can LDA be performed in PCA transformed space?", Patter Recognition 36, pp.563-566, 2003. https://doi.org/10.1016/S0031-3203(02)00048-1
  8. V. Vapnik. "The Natue of Statistical Learning Theory," Springer-verlag, New York, 1995.
  9. CJC. Burges. "A Tutorial on Support Vector Machines for Pattern Recognition," in Data Mining and Knowledge Discovery, v.2 n.2, pp.121-167. 1998. https://doi.org/10.1023/A:1009715923555
  10. E. Oauna, R. Freund, F. Girosi, "Training Support Vector Machines: An application ot face detection." Proceeding IEEE. CVPR, pp.130-136, 1997.
  11. Platt, J.C., "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines," Microsoft Research Technical Report MSR-TR-98-14, 1998.
  12. P. Liao, J. Liu, M. Wang, H. Ma, W. Zhang, "Ensemble local fractional LDA for Face Recognition", Computer Science and Automation Engineering(CSAE), 2012 IEEE International Conference on, Vol. 3, pp. 586-590, 2012.
  13. X. Bai, C. Wang, "Revised NMF with LDA based Color Face Recognition", Networking and Digital Society(ICNDS), 2010 2nd International Conference on, Vol. 1, pp. 156-159, 2010.
  14. Chengjun Liu, Wechsler, H., "Independent component analysis of Gabor feature for face recognition," Neural Networks, IEEE Transactions on, Volume: 14, Issue: 4, pages: 919-928, July 2003. https://doi.org/10.1109/TNN.2003.813829
  15. Buciu, I, kotropoulos, C, Pitas, I, "ICA and Gabor representation rot facial expression recognition," Image Processing, 2003. Proceeding. 2003 International Conference on, Volume:2, page. 14-17 Sept.2003.
  16. S. E. El-Khamy, O. Abdel-Alim, M. M. Saii, "Neural Network Face Recognition Using Statistical Feature Extraction", Radio Science Conference, 2000. 17th NRSC '2000. Seventeenth National, pp. C31/1-C31/8, 2000.
  17. Patrick K. Simpsion, "Fuzzy Min-Max Neural Networks - Part 1: Classification," IEEE Trans. on Neural Network, Vol. 3, No. 5, 1992.