Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

• Published in: Multimedia tools and applications Vol. 76; no. 1; pp. 333 - 354
• Main Authors: Leng, Lu, Li, Ming, Kim, Cheonshik, Bi, Xue
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2017; Springer Nature B.V
• Subjects: Access control; Accuracy; Algorithms; Analysis; Biometric identification; Biometrics; Computer Communication Networks; Computer engineering; Computer Science; Data Structures and Information Theory; Discrimination; Disturbances; Feature extraction; Feature recognition; Image coding; Multimedia; Multimedia Information Systems; Pattern recognition; Recognition; Sensors; Special Purpose and Application-Based Systems; Studies; Dual-source discrimination power analysis; Feature level fusion; Biometric fusion systems; Multi-instance contactless palmprint recognition; Two-dimensional discrete cosine transform
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-015-3058-7

Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.