caa a22 4500 606159959 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1843-x doi (NATIONALLICENCE)springer-10.1007/s00521-015-1843-x Extended collaborative neighbor representation for robust single-sample face recognition [Elektronische Daten] [Waqas Jadoon, Lei Zhang, Yi Zhang] Appearance-based methods in face recognition have become one of the dominant techniques in recent years. Several studies have demonstrated that feature space is no longer critical in face recognition when the probe image can be approximated by a linear combination of training samples. However, in real-world application of face recognition, there are limited or even a single training sample per subject available. The availability of insufficient training samples often results in poor generalization ability for the existing state-of-the-art appearance-based methods. In this work, to address single-sample per person problem, we extend collaborative representation-based classification using $$l_{2}$$ l 2 minimization approach, named extended collaborative neighbor representation (ECNR) that exploits the representational ability of training samples and intrapersonal variation from generic subjects. The proposed method is based on the assumption that intrapersonal variations from generic subjects are sharable across training dictionary. ECNR expresses a test sample as the linear combination of optimal neighbor bases from the single class-specific training dictionary and the intrapersonal variant bases from an auxiliary generic subjects dictionary with multiple samples per subject to represent the possible variations in testing and training samples. The resulting coding scheme is sparse in terms of original bases and is computationally efficient. Experiments on real-world face data sets have demonstrated the usefulness of the proposed method in single-sample face recognition tasks. The Natural Computing Applications Forum, 2015 Single-sample face recognition nationallicence Linear representation nationallicence Sparse representation nationallicence Classification nationallicence Linear regression nationallicence Jadoon Waqas Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China aut Zhang Lei Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China aut Zhang Yi Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1991-2000 0941-0643 26:8<1991 2015 26 521 https://doi.org/10.1007/s00521-015-1843-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1843-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jadoon Waqas Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China aut NATIONALLICENCE 700 1- Zhang Lei Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China aut NATIONALLICENCE 700 1- Zhang Yi Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1991-2000 0941-0643 26:8<1991 2015 26 521