A performance evaluation system is built for the open set face recognition task. The system consists of two parts: the quality evaluation for training set and the performance evaluation for classification result of test samples. For the former, Bhattacharyya distance is used to approximate the Bayesian error rate, and the particularity of open set problems that sample pairs do not obey the independent identical distribution assumptions is taken into account. The quality evaluation functions of the training set are obtained in both Gaussian or non-Gaussian distribution assumptions, and in Gaussian case this function has a closed form. For the latter, the distribution densities of the nearby positive and negative sample pairs are considered to measure the reliability of the similarity score given by a classifier. Therefore, the previous studies which are lacking of such measurements are complemented. The results in this paper are validated by experiments on multiple face databases.
Key words
Performance Evaluation /
Face Recognition /
Bhattacharyya Distance
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] Belhumeur P N, Hespanha J P, Kriegman D. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
[2] Comon P. Independent Component Analysis, A New Concept? Si-gnal Processing, 1994, 36(3): 287-314
[3] Goldberger J, Roweis S, Hinton G, et al. Neighborhood Components Analysis[EB/OL]. [2013-02-01]. http://www.eng.biu.ac.il/~goldbej/papers/ncanips.pdf
[4] Kailath T. The Divergence and Bhattacharyya Distance Measures in Signal Selection. IEEE Trans on Communication Technology, 1967, 15(1): 52-60
[5] Moghaddam B, Jebara T, Pentland A. Bayesian Face Recognition. Pattern Recognition, 2000, 33(11): 1771-1782
[6] Yang L, Jin R. Distance Metric Learning: A Comprehensive Survey[EB/OL]. [2013-02-01]. http://www.cs.cmu.edu/~liuy/frame_survey_v2.pdf
[7] Guillaumin M, Verbeek J, Schmid C. Is That You? Metric Learning Approaches for Face Identification // Proc of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan, 2009: 498-505
[8] Liu C J, Wechsler H. Enhanced Fisher Linear Discriminant Models for Face Recognition // Proc of the 14th International Conference on Pattern Recognition. Hong Kong, China, 1998, II: 1368-1372
[9] Nguyen H V, Bai L. Cosine Similarity Metric Learning for Face Ve-rification[EB/OL]. [2013-02-01]. http://link.springer.com/content/pdf/10.1007/978-3-642-19309-5_55.pdf
[10] Phillips P J, Flynn P J, Scruggs T, et al. Overview of the Face Recognition Grand Challenge // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, I: 947-954
[11] Sim T, Baker S, Bsat M. The CMU Pose, Illumination, and Expression (PIE) Database // Proc of the 5th IEEE International Conference on Automatic Face and Gesture Recognition. Washington, USA, 2002: 46-51
[12] Liu C J, Wechsler H. Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans on Image Processing, 2002, 11(4): 467-476
{{custom_fnGroup.title_en}}
Footnotes
{{custom_fn.content}}
PDF(810 KB)
