Abstract Pose variations of face images make recognition rate decrease significantly. The fused face image gained by several multi-pose face images of the same person is used to identify the face information of this person. The proposed fusion method includes texture information and geometry information, and the face image sets for fusion are selected through geometry information of face images to guarantee the integrity of the face information. On the basis of existing face databases, the recognition rates of the original face images and the fused face images composed of multi-pose face images from internet are tested respectively. The recognition results show that the fusion method achieves higher recognition rate.
[1] Abate A F, Nappi M, Riccio D, et al. 2D and 3D Face Recognition: A Survey. Pattern Recognition Letters, 2007, 28(14): 1885-1906 [2] Li W J, Wang C J, Zhang W, et al. A Survey of Face Recognition. Pattern Recognition and Artificial Intelligence, 2006, 19(1): 58-66 (in Chinese) (李武军,王崇骏,张 炜,等.人脸识别研究综述.模式识别与人工智能, 2006, 19(1): 58-66) [3] Jenkins R, Burton A M. 100% Accuracy in Automatic Face Recognition. Science, 2008, 319(5862): 435 [4] Georghiades A S, Belhumeur P N, Kriegman D J. From Few to Many: Illumination Cone Models for Face Recognition under Vari-able Lighting and Pose. IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660 [5] Husken M, Brauckmann M, Gehlen S, et al. Strategies and Benefits of Fusion of 2D and 3D Face Recognition // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition. San Diego, USA, 2005. DOI:10.1109/CVPR.2005.584 [6] Wagner A, Wright J, Ganesh A, et al. Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 34(2): 372-386 [7] Yang J, Liu Y L, Feng C S, et al. An Uncorrelated Space Algorithm Based on Fisher Minimum Criterion and Its Application to Face Recognition. Pattern Recognition and Artificial Intelligence, 2013, 26(6): 598-603 (in Chinese) (杨 军,刘妍丽,冯朝胜,等.Fisher极小准则不相关空间算法及其在人脸识别中的应用.模式识别与人工智能, 2013, 26(6): 598-603) [8] Xu S P, Liu X P, Li C Q, et al. Texture Descriptor Based on Spatial Statistical Features of Local Binary Pattern Code Pair. Pattern Recognition and Artificial Intelligence, 2013, 26(8): 769-776 (in Chinese) (徐少平,刘小平,李春泉,等.基于LBP值对空间统计特征的纹理描述符.模式识别与人工智能, 2013, 26(8): 769-776) [9] Cao Y, Miao Q G, Liu J C, et al. Advance and Prospects of AdaBoost Algorithm. Acta Automatica Sinica, 2013, 39(6): 745-758 (in Chinese) (曹 莹,苗启广,刘家辰,等.AdaBoost算法研究进展与展望.自动化学报, 2013, 39(6): 745-758) [10] Etemad K, Chellappa R. Discriminant Analysis for Recognition of Human Face Images. Journal of the Optical Society of America A, 1997, 14(8): 1724-1733 [11] Vetter T. Learning Novel Views to a Single Face Image // Proc of the 2nd International Conference on Automatic Face and Gesture Recognition. Killington, USA, 1996: 22-29 [12] Du C, Su G D, Lin X G, et al. Synthesis of Face Image with Pose Variations. Journal of Optoelectronics·Laser, 2004, 15(12): 1498-1501 (in Chinese) (杜 成,苏光大,林行刚,等.多姿态人脸图像合成.光电子·激光, 2004, 15(12): 1498-1501) [13] Zhang C P, Su G D. Human Face Recognition: A Survey. Journal of Image and Graphics, 2000, 5(A11): 885-894 (in Chinese) (张翠平,苏光大.人脸识别技术综述.中国图象图形学报, 2000, 5(A11): 885-894) [14] Viola P, Jones M. Rapid Object Detection Using a Boosted Cascade of Simple Features // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Kauai, USA, 2001, I: 511-518 [15] Edwards G J, Taylor C J, Cootes T F. Interpreting Face Images Using Active Appearance Models // Proc of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Shanghai, China, 1998: 300-305 [16] Wang K J, Zou G F. A Sub-Pattern Gabor Features Fusion Method for Single Sample Face Recognition. Pattern Recognition and Artificial Intelligence, 2013, 26(1): 50-56 (in Chinese) (王科俊,邹国锋.基于子模式的Gabor特征融合的单样本人脸识别.模式识别与人工智能, 2013, 26(1): 50-56)
2015, Vol. 28 
