Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96131
Title: Adaptive discriminant learning for face recognition
Authors: Kan, Meina
Shan, Shiguang
Su, Yu
Xu, Dong
Chen, Xilin
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2013
Source: Kan, M., Shan, S., Su, Y., Xu, D., & Chen, X. (2013). Adaptive discriminant learning for face recognition. Pattern recognition, 46(9), 2497-2509.
Series/Report no.: Pattern recognition
Abstract: Face recognition from Single Sample per Person (SSPP) is extremely challenging because only one sample is available for each person. While many discriminant analysis methods, such as Fisherfaces and its numerous variants, have achieved great success in face recognition, these methods cannot work in this scenario, because more than one sample per person are needed to calculate the within-class scatter matrix. To address this problem, we propose Adaptive Discriminant Analysis (ADA) in which the within-class scatter matrix of each enrolled subject is inferred using his/her single sample, by leveraging a generic set with multiple samples per person. Our method is motivated from the assumption that subjects who look alike to each other generally share similar within-class variations. In ADA, a limited number of neighbors for each single sample are first determined from the generic set by using kNN regression or Lasso regression. Then, the within-class scatter matrix of this single sample is inferred as the weighted average of the within-class scatter matrices of these neighbors based on the arithmetic mean or Riemannian mean. Finally, the optimal ADA projection directions can be computed analytically by using the inferred within-class scatter matrices and the actual between-class scatter matrix. The proposed method is evaluated on three databases including FERET database, FRGC database and a large real-world passport-like face database. The extensive results demonstrate the effectiveness of our ADA when compared with the existing solutions to the SSPP problem.
URI: https://hdl.handle.net/10356/96131
http://hdl.handle.net/10220/18072
ISSN: 0031-3203
DOI: http://dx.doi.org/10.1016/j.patcog.2013.01.037
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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