Soft Computing

, Volume 21, Issue 17, pp 4995–5002 | Cite as

A face recognition system based on convolution neural network using multiple distance face

Methodologies and Application

Abstract

The recognition technology that recognizes or discriminates certain individuals is very important for the security that provides intelligence services. Face recognition rate can vary depending on variability of the face itself as well as other external factors such as illumination, background, angle and distance of a camera position. The paper suggests a proper method for long-distance face recognition by resolving the change in recognition rate resulting from distance change in long-distance face recognition. For the long-distance face recognition test, face images by actual distance from 1 to 9 m away were obtained directly. Actual face images taken by distance were applied to resolve the issue rising from distance change and CNN was applied to extract overall features of face. The test showed that proposed face recognition algorithm that used CNN as feature extraction and face images by actual distance for training was found to show the best performance.

Keywords

Long-distance face recognition Multiple distance face Intelligent robot service Convolution neural network 

References

  1. Abdullah-Al-Wadud M, Kabir MH, Dewan MAA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600CrossRefGoogle Scholar
  2. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464CrossRefGoogle Scholar
  3. Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  4. Bevilacqua V, Cariello L, Carro G, Daleno D, Mastronardi G (2008) A face recognition system based on pseudo 2d hmm applied to neural network coefficients. Soft Comput 12(7):615–621CrossRefGoogle Scholar
  5. Byeon YH, Kwak KC (2014) Facial expression recognition using 3d convolutional neural network. Int J Adv Comput Sci Appl 5(12):107–112Google Scholar
  6. Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):705–741CrossRefGoogle Scholar
  7. Chen CH, Yao Y, Chang H, Koschan A, Abidi M (2013) Integration of multispectral face recognition and multi-ptz camera automated surveillance for security applications. Cent Eur J Eng 3(2):253–266Google Scholar
  8. Chen X, Liu W, Lai J, Li Z, Lu C (2012) Face recognition via local preserving average neighborhood margin maximization and extreme learning machine. Soft Comput 16(9):1515–1523CrossRefGoogle Scholar
  9. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New JerseyMATHGoogle Scholar
  10. Gonzalez RC (2009) Digital image processing. Pearson Education IndiaGoogle Scholar
  11. Kim HJ, Kim D, Lee J, Jeong IK (2015) Uncooperative person recognition based on stochastic information updates and environment estimators. ETRI J 37(2):395–405CrossRefGoogle Scholar
  12. Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113CrossRefGoogle Scholar
  13. Li B, Chang H, Shan S, Chen X (2010) Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Process Lett 17(1):20–23CrossRefGoogle Scholar
  14. Lv G (2011) Recognition of multi-fontstyle characters based on convolutional neural network. In: 2011 Fourth International Symposium on Computational Intelligence and Design (ISCID), IEEE, vol 2, pp 223–225Google Scholar
  15. Matsugu M, Mori K, Mitari Y, Kaneda Y (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5):555–559CrossRefGoogle Scholar
  16. Moon HM, Shin J, Shin J, Pan SB (2015) User authorization method based on face recognition for auto network access in home network system. Res Briefs Inf Commun Technol Evol 1(2015):1–13Google Scholar
  17. Niu XX, Suen CY (2012) A novel hybrid cnnsvm classifier for recognizing handwritten digits. Pattern Recognit 45(4):1318–1325CrossRefGoogle Scholar
  18. Park U, Choi HC, Jain AK, Lee SW (2013) Face tracking and recognition at a distance: a coaxial and concentric ptz camera system. IEEE Trans Inf Forensics Secur 8(10):1665–1677CrossRefGoogle Scholar
  19. Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33(3):1065–1076Google Scholar
  20. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  21. Wang Z, Miao Z, Wu QJ, Wan Y, Tang Z (2014) Low-resolution face recognition: a review. Vis Comput 30(4):359–386CrossRefGoogle Scholar
  22. Yann L, Leon B, Yoshua B, Patrick H (1998) Gradient-based learning applied to document recognition. Proc IEEE 88(11):2278–2324Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Convergence SciencesKongju National UniversityKongjuRepublic of Korea
  2. 2.Department of Electronics EngineeringChosun UniversityGwangjuRepublic of Korea

Personalised recommendations