植基於差異演化之類神經網路於臉部辨識應用

Face Recognition Using Neural Networks Based on Differential Evolution

朱振祥(Chen-Hsiang Chu)

指導教授 ： 林正堅 ； 王德譽

朝陽科技大學/資訊學院/資訊工程系/碩士(2009年)

摘要

臉部辨識已經廣泛的被應用在很多地方，多數的臉部辨識方法是聚焦在二維影像的探討，我們提出一個創新的方法結合二維臉部特徵資訊及三維的臉部深度資訊，接著我們使用賈伯小波強化二維影像的區域特徵向量在不同角度及方向的特徵資訊，然後我們結合二維臉部特徵資訊及三維臉部深度資訊，使用主成份分析法來取得二維臉部及三維臉部深度的特徵向量資訊，在辨識方面，我們提出植基於差異進化之多層類神經網路於臉部辨識應用。經實驗結果證明我們所提出的方法可以有效的辨識不同臉部表情及不同臉部姿勢的人臉。另外，我們也比較不同特徵數量的臉部資訊及測試在不同隱藏層數量，實驗結果證明我們提出的差異演化法擁有較穩定的辨識效果，也證明了結合二維臉部特徵及三維臉部表面特徵向量的方法，可以有效的提升臉部辨識率的效果。

關鍵字

人臉辨識 ； 賈伯小波 ； 主成份分析 ； 差異演化法 ； 多層類神經網路

並列摘要

Face recognition is a widely in many applications. Most of the approaches to face recognition have focused on the use of two dimensional images. We present an innovative method that combines two-dimensional texture and three-dimensional (3D) images surface feature vectors. Next, we use Gabor wavelets extracting local features at different scales and orientations by two-dimensional facial images. Next, we combine the texture with the three-dimensional (3D) images surface feature vectors based on principal component analysis (PCA) to obtain feature vectors from gray and facial surface images. We also propose a differential evolution (DE) algorithm for face recognition based on multilayer neural networks as an identification model. In ours experimental results demonstrate for the recognition different face poses and facial expressions method was efficiency. In addition, our work compared with different number of facial images feature vector and hidden nodes the experimental results demonstrated that the proposed differential evolution algorithm have a stable recognition rate. The texture and shape features can improve the efficiency of face recognition.

並列關鍵字

Face Recognition ； Gabor Wavelets ； Principal Component Analysis ； Differential Evolution ； Multilayer Neural Networks

參考文獻

[24] Cheng-Jian Lin, Ya-Tzu Houng, Chi-Yung Lee, “A PSO-Based Modified Multilayer Neural Networks for Face Recognition,” The Journal of Chaoyang University of Technology, 2007, No.12, pp.417-436.

[2] Te-Hsiu Sun, Ming-chih Chen, Shu-chuan Lo, Fang-Chih Tien, “Face recognition using 2D and disparity eigenface,” Expert Systems with Applications, 2007, Volume 33, Issue 2, pp. 265-273.

[3] Bing Luo, Yu-Jie Hao, Wei-Hua Zhang, Zhi-Shen Liu, “Comparison Of PCA And ICA In Face Recognition,” Apperceiving Computing and Intelligence Analysis, ICACIA 2008, pp. 241- 243.

[23] Y. T. Huang, C.-J. Lin and C. Y. Lee,” Face Recognition Using a Modified PSO-Based Multilayer Neural Network,” The 12th Conference on Artificial Intelligence and Applications, 2007, pp. 168.

[1] Wayo Puyati and Aranya Walairacht, “Efficiency Improvement for Unconstrained Face Recognition by Weightening Probability Values of Modular PCA and Wavelet PCA,” ICACT 2008. 10th International Conference on, Volume 2, pp. 1449 - 1453.