Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 37; no. 5; pp. 1067 - 1079
• Main Authors: Jiang, Xudong, Lai, Jian
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biometric Identification - methods; Databases, Factual; Dictionaries; Face; Face - anatomy & histology; Face recognition; Feature extraction; Humans; Image Processing, Computer-Assisted - methods; Sparse matrices; Training; Training data; Vectors; face recognition; low-rank matrix recovery; Sparse representation; dictionary learning; classification
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2014.2359453

Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases.