Face Recognition Method Based on Global and Local Features Fusion

doi:10.3969/j.issn.1006-2475.2014.03.026

Abstract

Abstract: In order to overcome the defects of current pre-processing method and the limitation of the sole facial feature recognition, four face recognition methods are studied based on the eyes-independent dynamic threshold-based algorithm of pre-processing .We extract global features using PCA and 2DPCA, and Gabor and LBP features for face local feature, analyze and compare the recognition rate of these features extraction methods and the situation which is suitable to these features. Based on the analysis of these features, feature fusion experiments are designed for PCA and LBP to improve the recognition accuracy. The experimental results show that the recognition rate is higher when using the decision fusion instead of the feature fusion and sole feature on the ORL and ESSEX database.

Key words: face recognition, global feature, local feature, 2DPCA, LBP, Gabor, feature fusion

CLC Number:

TP391.41

LAN Pei， FANG Chao. Face Recognition Method Based on Global and Local Features Fusion[J]. Computer and Modernization, 2014, 0(3): 109-113.

References

参考文献：
［1］Heisele B, Ho P, Poggio T. Face recognition with support vector machines: Global versus componentbased approach ［C］// Proceedings of the Eighth IEEE International Conference on Computer Vision. 2001:688-694.
［2］Shen L, Bai L. MutualBoost learning for selecting Gabor features for face recognition［J］. Pattern Recognition Letters, 2006,27(15):1758-1767.
［3］Ahonen T, Hadid A, Pietikinen M.Face recognition with local binary patterns［J］. Computer VisionECCV 2004, 2004,3021:469-481.
［4］Yang J, Frangi A F, Zhang D, et al. KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(2):230-244.
［5］Yang J, Zhang D, Frangi A F. Twodimensional PCA: A new approach to appearancebased face representation and recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(1):131-137.
［6］陶亮,庄镇泉. 复杂背景下人眼自动定位［J］. 计算机辅助设计与图形学学报, 2003,15(1):38-42.
［7］顾华,苏光大,杜成. 人脸关键特征点的自动定位［J］. 光电子·激光, 2004,15(8):975979.
［8］王丽婷,丁晓青,方驰. 基于随机森林的人脸关键点精确定位方法［J］. 清华大学学报: 自然科学版, 2009,49(4):543-546.
［9］方超,王斌斌,陈立生. 基于双眼独立动态阈值的人脸预处理及识别［J］. 电脑知识与技术, 2012,8(7):1618-1621.
［10］苏煜,山世光,陈熙霖,等. 基于全局和局部特征集成的人脸识别［J］. 软件学报, 2010,21(8):1849-1862.
［11］孙大瑞,吴乐南. 基于Gabor变换的人眼定位［J］. 电路与系统学报, 2001,6(4):29-32.
［12］钟荣清,李静. 一种基于决策融合的三维人脸识别新方法［J］. 信息与电脑:理论版, 2011(4):44-45.
［13］朱明旱,罗大庸. 2DFLD与LPP相结合的人脸和表情识别方法［J］. 模式识别与人工智能, 2009,22(1):60-63.
［14］Gupta S, Castleman K R, Markey M K, et al. Texas 3D face recognition database［C］// 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI). 2010:97-100.
［15］Gupta S, Markey M K, Bovik A C. Anthropometric 3D face recognition［J］. International Journal of Computer Vision, 2010,90(3):331-349.

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