Abstract：A classification algorithm based on multi-dimensional similarity distribution is presented to enhance the accuracy in open set face recognition. This algorithm firstly get the similarity vector distribution of known and unknown samples by testing on many labeled pictures. Then those similarity vectors are learned by linear discriminant analysis (LDA) to extract distribution features. Finally, the proposed algorithm rejects the unknown identity by feature-matching. Hence, the feature has strong classification ability in view of the discrimination information abstracted from the similarity distribution. Experimental results on several face databases demonstrate that the proposed method significantly outperforms the traditional method for open set face recognition.
张凯，苏剑波. 基于相似度分布的开集人脸识别方法[J]. 模式识别与人工智能, 2011, 24(1): 147-152.
ZHANG Kai, SU Jian-Bo. Open Set Face Recognition Approach Based on Similarity Distribution. , 2011, 24(1): 147-152.
[1] Jain A K, Ross A, Prabhakar S. An Introduction to Biometric Recognition. IEEE Trans on Circuits and Systems for Video Technology, 2004, 14(1): 4-20 [2] Shan Shiguang. Study on Some Key Issues in Face Recognition. Ph.D Dissertation. Beijing, China: Chinese Academy of Sciences. Institute of Computing Technology, 2004 (in Chinese) (山世光.人脸识别中若干关键问题的研究.博士学位论文. 北京:中国科学院计算技术研究所, 2004) [3] Phillips P J, Grother P, Micheals R J, et al. Face Recognition Vendor Test 2002: Evaluation Report. Technical Report, NISTIR 6965, Gaithersburg, USA: National Institute of Standards and Technology, 2003 [4] Shan Shiguang, Yang Peng, Chen Xilin, et al. AdaBoost Gabor Fisher Classifier for Face Recognition // Proc of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures. Beijing, China, 2005: 278-291 [5] Li Fayin, Wechsler H. Open Set Face Recognition Using Transduction. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1686-1697 [6] Duda R O, Hart P E, Stork D G. Pattern Classification. New York, USA: John Wiley Sons, 2001: 146-160 [7] Jain A K, Nandakumar K, Ross A A. Score Normalization in Multimodal Biometric Systems. Pattern Recognition, 2005, 38(12): 2270-2285 [8] Yang Fei, Shan Shiguang, Chen Xilin, et al. Using Score Normalization to Solve the Score Variation Problem in Face Authentication. New York, USA: Springer, 2005: 31-38 [9] Gammerman A, Vovk V, Vapnik V. Learning by Transduction // Proc of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, USA, 1998: 148-155 [10]Saunders C, Gammerman A. Vovk V. Tranjsduction with Confidence and Credibility // Proc of the 16th International Joint Conference on Artificial Intelligence. Stockholm, Sweden, 1999: 722-726 [11] Shao Jie, Huang Zi, Shen Hengtao, et al. Distribution-Based Similarity Measures for Multi-Dimensional Point Set Retrieval Applications // Proc of the 16th ACM International Conference on Multimedia. Vancouver, Canada, 2008: 429-438 [12] Yan Shuicheng, Xu Dong, Tang Xiaoou. Face Verification with Balanced Thresholds. IEEE Trans on Image Processing, 2007, 16(1): 262-268 [13] Kalocsai P, Zhao Wenyi, Biederman I. Face Similarity Space as Perceived by Humans and Artificial Systems // Proc of the 3rd International Conference on Face and Gesture Recognition. Nara, Japan, 1998: 177-180 [14] Chopra S, Hadsel R, LeCun Y. Learning a Similarity Metric Discriminatively with Application to Face Verification // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, Ⅰ: 539-546 [15] Phillips P J, Moon H, Rizvi S A, et al. The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(10): 1090-1104 [16] Gao Wen, Cao Bo, Shan Shiguang, et al. The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols. Technical Report, JDL_TR_04_FR_001, Beijing, China: Chinese Academy of Sciences. Joint Research Development Laboratory for Face Recognition, 2004 [17] Li S Z, Chu Rufeng, Liao Shengcai, et al. Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(4): 627-639
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