近年來生物資訊的應用蓬勃發展，其中利用生物特徵來做身分的識別更成為熱門的研究領域。人臉辨識因其取像容易且辨識過程為非接觸式，因此可應用的範圍廣泛並且成為生物特徵辨識的主流研究之一。然而在自然的環境下，人臉影像極易受到內在(表情、角度)及外在(光源)等因素的影響，這使得人臉辨識在生物特徵辨識領域中一直是個極具挑戰性的研究領域。
本論文主要利用局部區域以及多解析的分析方法來降低人臉影像因受局部破壞而導致辨識率下降的影響。特徵擷取以局部二元圖形(Local Binary Patterns)為基礎，並提出了利用LBP-TOP擷取出同時包含空間域以及解析域資訊的特徵。辨識的方法上以Distance Transform的距離計算取代了傳統以直方圖(histogram)為相似度計算的基礎，不僅提高了辨識的效果，在某些情況下它還具有降低特徵維度的作用。實驗的部分使用了ORL以及自行建置的人臉資料庫做為評測試驗，均獲得了不錯的效果。

In recent years, with the development of biometric applications, identity recognition using biometric information becomes the most hot research field. Face recognition has attracted more and more attention because of its enshrouded, non-contact properties and its wide range of applications, such as security monitoring and computer interaction. However, face recognition is still a most challenging research areas, because of the fact, in uncontrolled environments, the appearance of face will be deformed by variations of illumination, expression, pose and occlusion etc.
In this research, we mainly use the strategy of local region and multi-resolution analysis to reduce the impact caused by illumination, different expression and pose. The feature extracted method is based on Local Binary Patterns (LBP). Furthermore, we propose that using Local Binary Patterns-Three Orthogonal Planes (LBP-TOP) to extract the features that both include the spatial domain information and multi-scale information such that the extracted features are discriminative and robust. Feature matching method is replacing the similarity metric based on histogram with a local distance transform. Using local distance transform further improves the performance and, in some cases, it can reduce the dimension of feature.
The proposed method is evaluated with the ORL database and the self-built database. Experimental results demonstrate the good performance of our method.

[1]“International Biometric Group”.
http://www.biometricgroup.com/reports/public/market_report.php.
[2]R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, “Face Detection in Color Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 696-706, 2002.
[3]S. Birchfield, “Elliptical Head Tracking Using Intensity Gradients and Color Histograms”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 232-237, 1998.
[4]J. Terrillon, M. Sadek, H. Fukamachi, and S. Akamatsu, “Invariant Face Detection with Support Vector Machines”, The 15th International Conference on Patterns Recognition, vol. 4, pp. 210-217, 2000.
[5]P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features”, Computer Vision and Patterns Recognition, IEEE Computer Society Conference, vol. 1, pp. I-511-518, 2001.
[6]M. A. Turk and A. P. Pentland, “Face Recognition Using Eigenfaces”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
[7]P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
[8]W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zahang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel Non-Statistical Model for Face Representation and Recognition”, IEEE International Conference on Computer Vision, pp. 786-791, 2005.
[9]K. Tan and S. Chen, “Adaptively weighted Sub-Pattern PCA for Face Recognition”, Neurocomputing, vol. 64, pp. 505-511, 2005.
[10]Y. Freund and R. E. Schapire, “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting”, Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997.
[11]J. Zou, J. Qiang, and G. Nagy, ”A Comparative Study of Local Matching Approach for Face Recognition”, IEEE Transactions on Image Processing, vol. 16, no. 10, pp. 2617-2628, 2007.
[12]T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.
[13]B. Zhang, S. Shan, X. Chen, and W. Gao, “Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition”, IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 57-68, 2006.
[14]N. Tan, L. Huang, and C. Liu, “Face Recognition Based on a Single Image and Local Binary Pattern”, Second International Symposium on Intelligent Information Technology Application, pp. 366-370, 2008.
[15]C. Liu and H. Wechsler, “A Gabor Feature Classifier for Face Recognition”, IEEE International Conference on Computer Vision, vol. 2, pp. 270-275, 2001.
[16]T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp.971-987, 2002.
[17]H. Jin, Q. Liu, H.Lu, and X. Tong, “Face Detection Using Improved LBP under Bayesian Framework”, International Conference on Image and Graphics, pp. 306-309, 2004.
[18]S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Z. Li, “Learning Multi-scale Block Local Binary Patterns for Face Recognition”, ICB2007, LNCS 4642, pp. 828-837, 2007.
[19]藍偉銘, “基於空間群集模型與支援向量機之人臉辨識系統”, 國立台灣科技大學資訊工程系, 碩士論文, 2010.
[20]G. Zhao and M. Pietikainen, “Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, No. 6, pp.915-928, 2007.
[21]“CURet Database”.
http://www.cs.columbia.edu/CAVE/software/curet/.
[22]S. Liao and A. C. S. Chung, “Texture Classification By Using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2007.
[23]G. Borgefors, “Distance Transformations in Digital Images”, Computer Vision, Graphics and Image Processing, vol. 35, pp. 344-371, 1986.
[24]X. Tan and B. Triggs, “Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions”, Analysis and Modeling of Faces and Gestures, LNCS 4778, pp. 168-182, 2007.
[25]“ORL Database”.
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
[26]G. Aguilar-Torres, G. Sanchez-Perez, M. Nakano-Miyatake, and H. Perez-Meana, “Face Recognition Algorithm Using the Discrete Gabor Transform”, The 17th International Conference on Electronics, Communications and Computers, pp. 35-55, 2007.
[27]R. M. Mutelo, W. L. Woo, and S. S. Dlay, “Discriminant Analysis of The Two-Dimensional Gabor Features for Face Recognition”, IET Computer Vision, vol. 2, no. 2, pp. 37-49, 2008.
[28]S. Du and R. K. Ward, “Improved Face Representation by Nonuniform Multilevel Selection of Gabor Convolution Features”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 39, no. 6, pp. 1408-1419, 2009.
[29]D. R. Kisku, M. Tistarelli, J. K. Sing, and P. Gupta, “Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm”, IEEE Computer Vision and Pattern Recognition Workshops, pp. 60-65, 2009.
[30]W. Jianguo, Y. Wankou, Y. Hui, and Y. Jingyu, “ICA Based Minimum Discriminant Analysis and Its Application to Face Recognition”, IEEE Chinese Conference on Pattern Recognition, pp. 1-4, 2008.