Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review
Abstract
:1. Introduction
- (1)
- We completely introduce different types of open-set palmprint databases in real-world application scenarios.
- (2)
- To the best of our knowledge, this study is the first work to create a detailed and comprehensive summary and analysis for multiview palmprint recognition methods.
2. Generic Palmprint Databases and Image Preprocessing
2.1. Palmprint Dataset Construction
2.2. Palmprint ROI Segmentation
3. Multiview Palmprint Recognition
3.1. Multiview Feature Containers
3.2. Multiview Palmprint Representation
3.3. Analysis
- (1)
- Single-view palmprint representation methods usually have a satisfactory performance in a specific scene. However, when the application environment changes, the feature representation ability will decrease.
- (2)
- Since multiview feature learning can adopt different complementary types of features from diverse views, multiview palmprint representation methods can achieve stable recognition results by enhancing the palmprint feature expression.
- (3)
- Multiview palmprint recognition can adapt to more complex application scenarios, where single-view palmprint representation has significant application limitations.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Databases | Total Number | Individual Number | Contactless or Contact Based | Posing Variation | Year |
---|---|---|---|---|---|
CASIA | 5501 | 310 | Contactless | Small | 2005 |
GPDS | 1000 | 100 | Contactless | Small | 2011 |
TJI | 12,000 | 300 | Contactless | Small | 2017 |
NTU-PI-V1 | 7781 | 1093 | Contactless | Large | 2019 |
REST | 1945 | 179 | Contactless | Small | 2021 |
IITD | 2601 | 230 | Contactless | Small | 2006 |
PolyU | 6000 | 500 | Contact based | No | 2003 |
PV_790 | 5180 | 2109 | Contact based | No | 2018 |
M_NIR | 6000 | 500 | Contact based | No | 2009 |
M_Blue | 6000 | 500 | Contact based | No | 2009 |
Methods | Authors | Ref. | Year |
---|---|---|---|
Active shape model | Gao, F.; Cao, K. | [48] | 2018 |
Active appearance model | Aykut, M.; Ekinci, M. | [49] | 2015 |
Ensemble of Regression Trees | Kazemi, V.; Sullivan, J. | [50] | 2014 |
Key point detection | Matkowski, W. M.; Chai, T.; Kong, A. W. K. | [51] | 2019 |
CNN Transfer | Izadpanahkakhk, M.; Razavi, S | [52] | 2018 |
Palmprint Detector | Liu, Y.; Kumar, A. | [53] | 2018 |
On-screen guide | Leng, L.; Gao, F.; Chen, Q.; Kim, C | [54] | 2018 |
Whole image | Afifi, M. | [55] | 2019 |
Robust adaptive hyperspectral ROI extraction | Zhao, S.; Zhang, B. | [12] | 2019 |
Methods | Ref. | Year | Description | Classifier | Database |
---|---|---|---|---|---|
LDDBP | [1] | 2019 | Dominant direction learning | Chi-square distance | CASIA IITD TJI HFUT |
LCDDP | [2] | 2021 | Complete and discriminative direction | Euclidean distance | PolyU IITD CASIA GPDS REST M_NIR PV_780 |
Local texture descriptor | [61] | 2020 | Local orientation encoding | distance | TJI CASIA GPDS IITD |
Principal lines | [62] | 2004 | Principal lines detection | Chi-square distance | PolyU |
Ordinal palmprint feature | [63] | 2005 | Ordinal direction encoding | Hamming distance | PolyU |
DDR | [9] | 2020 | Deep discriminative representation | Euclidean distance | CASIA, IITD, M_NIR, M_B, M_G, M_R |
JDCFR | [42] | 2019 | Joint deep representation | Euclidean distance | Hyperspectral palmprint database |
PalmNet | [4] | 2019 | Deep palmprint representation | Euclidean distance | CASIA, IITD, REST, TJI |
ALDC | [64] | 2019 | Local apparent latent direction | Chi-square distance | PolyU, IITD, GPDS, CASIA |
Class | Methods | Ref. | Year | Brief Description | Contributions to MFL |
---|---|---|---|---|---|
LSR | LC_LSR | [67] | 2019 | Local structure preservation | Provide a consistency subspace for feature enhancement. |
DS_LSR | [71] | 2020 | Self-representation for local structure capture | ||
LIS_LSR | [72] | 2022 | Low-rank structure learning for robust representation | ||
LR-R | LRR | [73] | 2011 | Latent low-rank structure representation | Provide a low-rank representation for robust recognition |
LRP | [74] | 2017 | Low-rank preserving embedding | ||
LR_IMF | [75] | 2017 | Low-rank projection learning | ||
Graph Learning | LRS_LSG | [76] | 2021 | Learning graph similarity | Utilize the local structures between different samples for representation enhancement |
DAG_SC | [77] | 2018 | Dynamic affinity graph construction | ||
LRAG | [78] | 2021 | Low-rank adaptive graph embedding | ||
SR | SPP | [79] | 2010 | Sparsity preserving projections | Structure reconstruction and robust representation |
CRP | [80] | 2015 | Collaborative-representation-based projections | ||
SSL | [81] | 2013 | Sparse subspace clustering |
Methods | Ref. | Year | Description | Databases | Application Categories |
---|---|---|---|---|---|
DC_MDPR | [27] | 2022 | Descriminative representation with double-cohesion learning | CASIA, IITD, GPDS, TJI, M_NIR, PV_790 | Contact based, contactless, palm–vein |
SSL_RMPR | [34] | 2022 | Multiview representation in the same sub space | CASIA, IITD, GPDS, TJI, PV_790, DHV_860 | Contact based, contactless, palm–vein, dorsal hand vein |
JMvFL | [22] | 2020 | Joint multiview feature learning | PolyU, CASIA, TJI, GPDS, PolyU_FKP | Contact-based, contactless, finger-knuckle-print |
PR_MFR | [83] | 2016 | Multi-feature integration with local and global features | PolyU | Contact based |
MFF_CRPR | [84] | 2018 | Multi-feature fusion using collaborative residual | Hyperspectral palmprint database | Hyperspectral |
GMF Encoding | [85] | 2020 | Joint multiple-type features encoding | CASIA, IITD, TJI | Contactless |
MFF_UPR | [86] | 2018 | Multiple feature fusion on feature level | CASIA, IITD, PolyU | Contact based, contactless |
MHPR | [87] | 2010 | Feature fusion with four types of features | High-resolution palmprint database | High resolution |
MSCNN | [88] | 2022 | Multi-stream CNN fausion | PolyU, M_B, HFUT, TJI | Contact based, contactless |
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Zhao, S.; Fei, L.; Wen, J. Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review. Mathematics 2023, 11, 1261. https://doi.org/10.3390/math11051261
Zhao S, Fei L, Wen J. Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review. Mathematics. 2023; 11(5):1261. https://doi.org/10.3390/math11051261
Chicago/Turabian StyleZhao, Shuping, Lunke Fei, and Jie Wen. 2023. "Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review" Mathematics 11, no. 5: 1261. https://doi.org/10.3390/math11051261
APA StyleZhao, S., Fei, L., & Wen, J. (2023). Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review. Mathematics, 11(5), 1261. https://doi.org/10.3390/math11051261