Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Key Aboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China 在期刊界中查找 在百度中查找 在本站中查找
Pulse Coupled Neural Network (PCNN) is a new generation artificial neural network (ANN) based on biological vision. It has wide application prospects in the field of digital image processing and artificial intelligence. In this paper, we propose a method to extract face features by studying PCNN theoretical model and its working characteristics. Firstly, the low frequency feature of face image is extracted by wavelet transform. Then, the simplified PCNN is used to extract the corresponding time series of face image reconstructed by wavelet low-frequency coefficient, which is used as the feature sequence of face recognition. Finally, the face recognition process is completed with time series and Euclidean distance. In this paper, we demonstrate the effectiveness of the method with ORL face database.
[2] Lu GC, Lin CY. PCA based immune networks for human face recognition. Applied Soft Computing, 2011, 11(2): 1743-1752.[DOI:10.1016/j.asoc.2010.05.017]
[3] Shao H, Chen S, Zhao JY, et al. Face recognition based on subset selection via metric learning on manifold. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12): 1046-1058.
[5] Shchegoleva NL, Kukharev GA. Application of two-dimensional principal component analysis for recognition of face images. Pattern Recognition and Image Analysis, 2010, 20(4): 513-527.[DOI:10.1134/S1054661810040127]