Improved Cross-Label Suppression Dictionary Learning for Face Recognition

• Published in: IEEE access Vol. 6; pp. 48716 - 48725
• Main Authors: Zhou, Tian, Yang, Sujuan, Wang, Lei, Yao, Jiming, Gui, Guan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; compressive sensing; computational complexity; Computational modeling; Computing costs; Cross-label suppression; Dictionaries; dictionary learning; Face recognition; Machine learning; Representations; Signal reconstruction; Time complexity; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2868133

Cross-label suppression dictionary learning is an effective approach to preserve the label property for signal representation in face recognition. This paper presents a proposed improved dictionary learning algorithm, considering the tradeoffs between the operating time and the signal reconstruction residuals for the face recognition problem that combines an optimal loss function and the cross-label suppression supervised dictionary learning approach. Based on the relationship of the cost time of the dictionary learning algorithm and the residuals of the sparse representations, this paper attempts to select an optimal sparse coding dimension for the original signal to reduce the computational cost. Experiments on face recognition confirm that our proposed algorithm is able to achieve a desired classification results as well as obtain a considerably faster dictionary learning process.