Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis
Abstract
:1. Introduction
- -
- We proposed a feature extraction approach for contactless palmprint recognition based on DWT multiresolution analysis and multi-scale BSIF description.
- -
- We substituted the classical approach of multi-block (i.e., multi-patch) decomposition with DWT multiresolution analysis.
- -
- We conducted more profound experiments and analyses on the pre-processing, feature extraction, and classification stages by testing and considering several configurations to find the best-performing parameters that maximize the recognition rate.
2. Related Work
2.1. Coding-Based Methods
2.2. Texture-Descriptors-Based Methods
2.3. Deep Learning-Based Methods
3. Proposed Approach
3.1. Pre-Processing
3.2. Feature Extraction
3.2.1. Theoretical Principle of the Employed Methods
- A.
- Discrete Wavelet Transform (DWT)
- B.
- Binarized Statistical Image Features (BSIF)
3.2.2. Proposed Feature Extraction Approach—Multiresolution Analysis
3.3. Classification
3.3.1. K-Nearest Neighbors (K-NN) Classifier
3.3.2. Centroid Displacement-Based K-Nearest Neighbor (CDNN) Classifier
4. Experimental Analysis
4.1. Datasets
4.1.1. IITD Touchless Palmprint Database (Version 1.0)
4.1.2. CASIA Palmprint Database
4.2. Setups
4.3. Experiment #1 (Effects of the BSIF Parameters)
4.4. Experiment #2 (Effects of the Multiresolution Analysis)
4.5. Experiment #3 (Effects of the Wavelet Family)
4.6. Experiment #4 (Effects of the Pre-Processing)
4.7. Experiment #5 (Effects of Classification)
4.8. Comparison
- -
- The texture extractor analyses the picture pixel by pixel, i.e., we consider the advantages of local information.
- -
- The picture is analyzed at several levels, i.e., we exploit multi-level information.
- -
- The extracted occurrences from each level are collected in a histogram, i.e., we operate with global information.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Approach | Publication | Method | Employed Dataset | Evaluation Protocol | ||
---|---|---|---|---|---|---|
Name | #Sub. | #img. | ||||
Coding-based methods | Kong and Zhang [20] | CompCode | IITD | 230 | 2600 | 4 img/sub randomly selected Train and remaining Test |
CASIA | 312 | 5500 | ||||
Sun et al. [19] | OrdinalCode | IITD | 230 | 2601 | 4/5 img/sub Train and remaining Test | |
CASIA | 312 | 5502 | ||||
Zhang et al. [21] | E-BOCV | IITD | 230 | 2601 | 2 img/sub Train and remaining Test | |
CASIA | 312 | 5502 | 3 img/sub Train and remaining Test | |||
Fei et al. [22] | DOC | IITD | 230 | 2601 | 4/5 img/sub Train and remaining Test | |
CASIA | 312 | 5502 | ||||
Xu et al. [23] | DRCC | IITD | 230 | 2600 | 4 img/sub Train and remaining Test | |
PolyU II | 193 | 7752 | ||||
MPolyU | 250 | 6000 | ||||
Texture-based methods | Michael et al. [24] | DGLBP | CASIA | 312 | 5500 | 4 img/sub Train and remaining Test |
IITD | 230 | 2601 | ||||
Morales et al. [25] | SIFT_OLOF | CASIA | 312 | 5500 | 4 img/sub Train and 4 img/sub remaining Test | |
IITD | 230 | 2601 | ||||
Hammami et al. [26] | LBP | CASIA | 282 | 5412 | 4 img/sub Train and 4 img/sub remaining Test | |
PolyU | 193 | 7752 | ||||
Wu et al. [27] | SIFT_IRANSAC_OLOF | CASIA | 312 | 5500 | 4 img/sub Train and 4 img/sub remaining Test | |
IITD | 230 | 2601 | ||||
Luo et al. [28] | LLDP_MFRAT | CASIA | 312 | 5500 | 4 img/sub Train and 4 img/sub remaining Test | |
IITD | 230 | 2601 | ||||
LLDP_Gabor | CASIA | 312 | 5500 | |||
IITD | 230 | 2601 | ||||
Wu et al. [29] | TFD | CASIA | 312 | 5502 | 4 img/sub Train and remaining Test | |
IITD | 230 | 2601 | ||||
Deep learning-based methods | Izadpanahkakhk et al. [30] | FEM | IITD | 230 | 2600 | 4 img/sub Train and remaining Test |
HKPU | 193 | 7752 | Images from first session Train and Images from second session Test | |||
Fei et al. [5] | AlexNet | IITD | 230 | 2600 | 4 img/Sub randomly selected Train and remaining Test | |
VGG-16 | GPDS | 100 | 1000 | |||
Inception-V3 | CASIA | 312 | 5500 | |||
ResNet-50 | ||||||
Zhao and Zhang [31] | DDR | IITD | 230 | 2600 | 4 img/Sub randomly selected Train and remaining Test | |
CASIA | 312 | 5500 |
5 Bits | 6 Bits | 7 Bits | 8 Bits | 9 Bits | 10 Bits | 11 Bits | 12 Bits | |
---|---|---|---|---|---|---|---|---|
3 × 3 | 49.15 | 60.11 | 62.37 | 63.99 | / | / | / | / |
5 × 5 | 56.99 | 70.26 | 74.47 | 77.02 | 78.51 | 78.76 | 78.76 | 77.10 |
7 × 7 | 67.07 | 76.53 | 82.92 | 87.05 | 85.47 | 87.62 | 89.03 | 90.69 |
9 × 9 | 70.55 | 80.09 | 85.59 | 86.40 | 89.64 | 92.96 | 93.85 | 94.25 |
11 × 11 | 71.88 | 82.76 | 88.83 | 88.47 | 91.30 | 93.97 | 94.61 | 95.63 |
13 × 13 | 73.70 | 81.39 | 88.87 | 89.23 | 92.47 | 95.34 | 94.98 | 96.03 |
15 × 15 | 75.20 | 83.09 | 89.40 | 90.77 | 93.32 | 94.29 | 95.67 | 96.60 |
17 × 17 | 75.44 | 83.37 | 89.07 | 91.70 | 93.68 | 94.53 | 95.95 | 96.35 |
5 Bits | 6 Bits | 7 Bits | 8 Bits | 9 Bits | 10 Bits | 11 Bits | 12 Bits | |
---|---|---|---|---|---|---|---|---|
3 × 3 | 25.88 | 34.64 | 38.49 | 37.59 | / | / | / | / |
5 × 5 | 35.87 | 46.35 | 53.56 | 53.56 | 51.51 | 51.67 | 52.17 | 52.66 |
7 × 7 | 45.53 | 58.72 | 58.72 | 66.09 | 67.15 | 72.48 | 72.48 | 75.18 |
9 × 9 | 48.81 | 62.65 | 72.89 | 75.51 | 76.65 | 82.47 | 82.88 | 84.93 |
11 × 11 | 53.48 | 67.97 | 78.70 | 79.93 | 84.93 | 88.20 | 89.84 | 90.58 |
13 × 13 | 57.08 | 69.20 | 79.52 | 82.96 | 86.81 | 91.31 | 92.38 | 93.77 |
15 × 15 | 57.73 | 71.99 | 80.34 | 83.94 | 89.02 | 91.15 | 93.85 | 94.67 |
17 × 17 | 57.08 | 71.90 | 80.83 | 85.58 | 89.68 | 91.64 | 94.34 | 95.49 |
DWT Families | Haar | Daubechies | Biorthogonal | Coiflets | Symlets | Fejer-Korovkin Filters | Reverse Biorthogonal | Discrete Meyer | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Results | 96.23 | db1 | 96.23 | bior1.1 | 96.23 | coif1 | 96.15 | sym2 | 96.15 | fk4 | 96.06 | rbio1.1 | 96.23 | 96.15 |
db2 | 96.15 | bior2.2 | 96.23 | coif2 | 96.39 | sym3 | 96.06 | fk6 | 96.15 | rbio2.2 | 96.15 | |||
db3 | 96.06 | bior3.1 | 96.15 | coif3 | 96.15 | sym4 | 96.15 | fk8 | 96.31 | rbio3.1 | 96.15 | |||
db4 | 96.39 | bior3.9 | 96.31 | coif4 | 96.39 | sym5 | 96.15 | fk14 | 96.31 | rbio3.9 | 96.31 |
DWT Families | Haar | Daubechies | Biorthogonal | Coiflets | Symlets | Fejer-Korovkin Filters | Reverse Biorthogonal | Discrete Meyer | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Results | 96.92 | db1 | 96.92 | dior1.1 | 96.92 | coif1 | 96.88 | sym2 | 96.92 | fk4 | 96.96 | rbio1.1 | 96.92 | 96.19 |
db2 | 96.92 | dior2.2 | 96.92 | coif2 | 97.04 | sym3 | 96.88 | fk6 | 96.92 | rbio2.2 | 96.88 | |||
db3 | 96.88 | dior3.1 | 96.96 | coif3 | 97.08 | sym4 | 96.92 | fk8 | 97.00 | rbio3.1 | 96.92 | |||
db4 | 97.04 | dior3.9 | 97.04 | coif4 | 97.08 | sym5 | 97.04 | fk14 | 97.00 | rbio3.9 | 97.12 |
Databases | Without Pre-Processing (%) | Pre-Processing with Median Filtering (%) | Pre-Processing with CLAHE (%) |
---|---|---|---|
IITD | 96.39 | 95.99 | 97.13 |
CASIA | 97.12 | 96.60 | 97.21 |
CASIA | IITD | |||||||
---|---|---|---|---|---|---|---|---|
K | K-NN | CDNN | K-NN | CDNN | ||||
Euclidean | City Block | Euclidean | City Block | Euclidean | City Block | Euclidean | City Block | |
1 | 97.21 | 98.10 | 97.21 | 98.10 | 97.13 | 98.77 | 97.13 | 98.77 |
3 | 96.68 | 97.73 | 96.68 | 97.69 | 95.91 | 98.28 | 95.91 | 98.12 |
5 | 96.08 | 97.29 | 96.60 | 97.41 | 93.78 | 97.13 | 94.02 | 97.13 |
7 | 95.15 | 96.68 | 95.71 | 97.01 | 92.63 | 96.48 | 93.12 | 96.64 |
9 | 94.50 | 96.08 | 95.50 | 96.76 | 90.91 | 95.33 | 91.89 | 95.74 |
Publication | Year | Method | IITD (%) | CASIA (%) | |
---|---|---|---|---|---|
Coding-based methods | Kong and Zhang [20] | 2004 | CompCode | 77.79 | 79.27 |
Sun et al. [19] | 2005 | OrdinalCode | 73.26 | 73.32 | |
Zhang et al. [21] | 2012 | E-BOCV | 85.93 | 84.06 | |
Fei et al. [22] | 2016 | DOC | 89.99 | 78.51 | |
Xu et al. [23] | 2018 | DRCC | 88.82 | / | |
Texture-based methods | Michael et al. [24] | 2008 | DGLBP | 76.44 | 78.86 |
Morales et al. [25] | 2011 | SIFT_OLOF | 89.44 | 89.99 | |
Hammami et al. [26] | 2014 | LBP | / | 96.66 | |
Wu et al. [27] | 2014 | SIFT_IRANSAC_OLOF | 93.28 | 91.46 | |
Luo et al. [28] | 2016 | LLDP_MFRAT | 92.75 | 90.77 | |
LLDP_Gabor | 95.17 | 93.00 | |||
Wu et al. [29] | 2021 | TFD | 97.47 | 96.88 | |
Deep learning-based methods | Izadpanahkakhk et al. [30] | 2018 | FEM | 94.70 | / |
Fei et al. [5] | 2019 | AlexNet | 88.18 | 94.91 | |
VGG-16 | 92.12 | 94.01 | |||
Inception-V3 | 96.22 | 93.85 | |||
ResNet-50 | 95.57 | 95.21 | |||
Zhao and Zhang [31] | 2020 | DDR | 98.70 | 99.41 | |
Our Approach | 2022 | BSIF + DWT | 98.77 | 98.10 |
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Amrouni, N.; Benzaoui, A.; Bouaouina, R.; Khaldi, Y.; Adjabi, I.; Bouglimina, O. Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis. Sensors 2022, 22, 9814. https://doi.org/10.3390/s22249814
Amrouni N, Benzaoui A, Bouaouina R, Khaldi Y, Adjabi I, Bouglimina O. Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis. Sensors. 2022; 22(24):9814. https://doi.org/10.3390/s22249814
Chicago/Turabian StyleAmrouni, Nadia, Amir Benzaoui, Rafik Bouaouina, Yacine Khaldi, Insaf Adjabi, and Ouahiba Bouglimina. 2022. "Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis" Sensors 22, no. 24: 9814. https://doi.org/10.3390/s22249814
APA StyleAmrouni, N., Benzaoui, A., Bouaouina, R., Khaldi, Y., Adjabi, I., & Bouglimina, O. (2022). Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis. Sensors, 22(24), 9814. https://doi.org/10.3390/s22249814