Browse > Article
http://dx.doi.org/10.5370/KIEE.2015.64.6.900

A Study On Three-dimensional Optimized Face Recognition Model : Comparative Studies and Analysis of Model Architectures  

Park, Chan-Jun (Dept. of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
Kim, Jin-Yul (Dept. of Electronic Engineering, The University of Suwon)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.64, no.6, 2015 , pp. 900-911 More about this Journal
Abstract
In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.
Keywords
3D Face Recognition; RBFNNs; PNN; Artificial Bee Colony; Multiple Point Signature; Particle Swarm Optimization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 B. J, Park, S. K. Oh, and W. Pedrycz. "The design of polynomial function-based neural network predictors for detection of software defects." Information Sciences Vol 229 pp. 40-57 2013.   DOI   ScienceOn
2 W. Huang, S. K. Oh, W. Pedrycz, "Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)" Neural Networks, Vol 60, pp 166-181, December 2014   DOI   ScienceOn
3 S. K. Oh, W. D. Kim, and W. Pedrycz, "Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization." Fuzzy Sets and Systems, Vol 237, pp 96-117, February 2014   DOI   ScienceOn
4 S. B. Roh, S. K. Oh, and W. Pedrycz. "Design of fuzzy radial basis function-based polynomial neural networks." Fuzzy sets and systems Vol 185, pp 15-37. December 2011   DOI   ScienceOn
5 Ahn, T Chon, et al. "Design of Radial Basis Function Classifier Based on Polynomial Neural Networks." Soft Computing in Artificial Intelligence. Springer International Publishing, Vol 270, pp 107-115, 2014   DOI
6 S-K. Oh, W-D. Kim, and W. Pedrycz,“Polynomial based radial basis function neural networks (P-RBFNNs) realized with the aid of particle swarm optimization,” Fuzzy Sets and Systems, Vol. 163, No. 1, pp. 54-77, 2011   DOI   ScienceOn
7 Y. Wang, C. Chua and Y. Ho, “Facial Feature Detection and Face Recognition from 2D and 3D Images," Pattern Recognition Letters, vol. 23, pp. 1191-1202, 2002.   DOI   ScienceOn
8 C. Chua, R. Jarvis, "Point Signature: A New representation for 3D Object Recognition," International Journal of Computer Vision Vol. 25, No.1, pp. 63-85, 1997.   DOI
9 Karaboga, Dervis, et al. "A comprehensive survey: artificial bee colony (ABC) algorithm and applications." Artificial Intelligence Review Vol 42, lssue 1 pp 21-57, June 2014.   DOI   ScienceOn
10 Kaveh, A. "Particle Swarm Optimization." Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, pp 9-40. Mar 2014