This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication

Year(s) from:  to 
Keywords (separated by spaces):

Sparse Variation Dictionary Learning for Face Recognition with A Single Training Sample Per Person

Meng Yang, Luc Van Gool, and Lei Zhang
Proc. 14th IEEE International Conf. Computer Vision (ICCV)
December 2013, in press


Face recognition (FR) with a single training sample per person (STSPP) is a very challenging problem due to the lack of information to predict the variations in the query sample. Sparse representation based classification has shown interesting results in robust FR; however, its perfor-mance will deteriorate much for FR with STSPP. To address this issue, in this paper we learn a sparse variation dic-tionary from a generic training set to improve the query sample representation by STSPP. Instead of learning from the generic training set independently w.r.t. the gallery set, the proposed sparse variation dictionary learning (SVDL) method is adaptive to the gallery set by jointly learning a projection to connect the generic training set with the gallery set. The learnt sparse variation dictionary can be easily integrated into the framework of sparse representa-tion based classification so that various variations in face images, including illumination, expression, occlusion, pose, etc., can be better handled. Experiments on the large-scale CMU Multi-PIE, FRGC and LFW databases demonstrate the promising performance of SVDL on FR with STSPP.

Download in pdf format
  author = {Meng Yang and Luc Van Gool and and Lei Zhang},
  title = {Sparse Variation Dictionary Learning for Face Recognition with A Single Training Sample Per Person},
  booktitle = {Proc. 14th IEEE International Conf. Computer Vision (ICCV)},
  year = {2013},
  month = {December},
  keywords = {},
  note = {in press}