S3RC

Codes for Semi-Supervised Sparse Representation based Classificatin (S3RC), Version 1.0

Please refer to our following paper for algorithm details:

Yuan Gao, Jiayi Ma, and Alan L. Yuille. "Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples", IEEE Transactions on Image Processing, 26(5), pp. 2545-2560, May 2017.

Usage:

• run S3RC_single_labeled_sample.m to start the demo for Face Recognition with insufficient labeled samples;
• run S3RC_insufficient_labeled_sample.m to start the demo for Face Recognition with single labeled sample per person.
• The demo codes needs L1_homotopy_v2.0 toobox to solve the L1 minimization problem (already included), which can be acquired from http://www.ece.ucr.edu/~sasif/homotopy/.

Features:

• This release contains the vanilla version of our S3RC algorithm, i.e., our main contribution on the gallery dictionary learning with basic ESRC variation dictionary.
• In order to combine more advanced variation dictionary, e.g., S3RC-SVDL, S3RC-RADL, or your own variation dictionary learning, just replace the variation dictionary V in S3RC_single_labeled_sample.m or S3RC_insufficient_labeled_sample.m.

Contacts

For questions about the code or the paper, feel free to contact Yuan Gao by [email protected].