Multimedia Tools and Applications

, Volume 77, Issue 16, pp 21371–21392 | Cite as

Sparse factorial code representation using independent component analysis for face recognition

  • Chao Li
  • Jian-Xun MiEmail author


This paper presents a new face recognition method based on Independent Component Analysis (ICA), named Sparse Factorial Code Representation (SFCR). The SFCR employs the architecture II of ICA (ICAII) to achieve sparse facial codes, which seeks for a representation that generates encoding coefficients with the statistically independent property, i.e., factorial coding. In ICAII the coefficients of training samples are ‘natural’ sparse, but coefficients for test samples are not as sparse as that of training samples according to comprehensive experimental results. We believe that the generating process of the latter is contaminated by projection matrixes of the training samples which do not contain any information about the test samples, which makes the coefficients encoding non-consistency. As a result, the small values in the non-sparse encoding coefficients of a test sample, which are caused by noise and usually influence the representation of independent components, will increase the probability of misclassification in the recognition of facial patterns. To ensure the sparsity of the coefficients of test samples and encoding consistency, l1 -norm optimization based sparse constraint technology is employed in SFCR. The SFCR is evaluated on several public available datasets such as AR, ORL, Extended-Yale B, FERET, and LFW databases. The experimental results demonstrate the good performance of our method.


Face recognition Independent component analysis Sparse factorial code 



This study was funded by the National Nature Science Foundation of China (Grant Nos. 61601070 and 61403053) and Chongqing Education Committee (Grant Nos. KJ1500402 and KJ1500417).


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Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of EducationGuangzhouChina

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