A face recognition system based on convolution neural network using multiple distance face
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.
KeywordsLong-distance face recognition Multiple distance face Intelligent robot service Convolution neural network
The work was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Republic of Korea (2011-0029927) and the Ministry of Trade, Industry and Energy(MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the Promoting Regional specialized Industry.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Byeon YH, Kwak KC (2014) Facial expression recognition using 3d convolutional neural network. Int J Adv Comput Sci Appl 5(12):107–112Google Scholar
- 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
- Gonzalez RC (2009) Digital image processing. Pearson Education IndiaGoogle Scholar
- 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
- 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
- Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33(3):1065–1076Google Scholar
- Yann L, Leon B, Yoshua B, Patrick H (1998) Gradient-based learning applied to document recognition. Proc IEEE 88(11):2278–2324Google Scholar