A New Face Recognition Algorithm based on Dictionary Learning for a Single Training Sample per Person

Yang Liu and Ian Wassell

Abstract

The number of the training samples per person has a significant impact on face recognition (FR) performance. For the single training sample per person (STSPP) problem, most traditional FR algorithms exhibit performance degradation owing to the limited information available to predict the variance of the query sample. This paper proposes a new method for the STSPP problem in FR, namely the Learn-Generate-Classify (LGC) method. The LGC method first learns the relationship between the multiple images of a subject based on dictionary learning from a generic training set. Then it predicts the intra-class variance of the gallery set using the learned relationship. Based on the predicted information, synthetic samples can be generated, thus extending the single sample gallery set to one having multiple samples. Finally, we can classify the query samples using the traditional sparse representation classification (SRC) framework on the multi-sample gallery set. We verified the effectiveness of the new LGC method on the CMU Multi-pie database, with different illumination, expression and pose variation factors. It shows that the LGC method demonstrates a promising FR performance with only a STSPP.

Session

Poster 1

Files

Extended Abstract (PDF, 130K)
Paper (PDF, 380K)

DOI

10.5244/C.29.69
https://dx.doi.org/10.5244/C.29.69

Citation

Bibtex

@inproceedings{BMVC2015_69,
	title={A New Face Recognition Algorithm based on Dictionary Learning for a Single Training Sample per Person},
	author={Yang Liu and Ian Wassell},
	year={2015},
	month={September},
	pages={69.1-69.11},
	articleno={69},
	numpages={11},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.69},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.69}
}