Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor
Abstract
:1. Introduction
Category | Method | Advantages | Disadvantage | |
---|---|---|---|---|
Multiple sensor-based method | -Evaluating the five soiling levels of Euro banknotes by using various sensors [4]. | Using information by various sensors allows for the extraction of more discriminating features | -Focuses on analyzing the soiling property of the banknotes without proposing a solution for automatically classifying the fitness of banknotes [4]. | |
-Denomination classification using visible and IR sensors [6]. | -Mainly focuses on denomination classification [6] and counterfeit banknote detection [13]. | |||
-Detecting fake banknotes by CCD cameras with visible, UV, and IR lights [13]. | -Using multiple sensors leads to complexity in hardware implementation and an increase in processing time with multiple images from multiple sensors. | |||
Single sensor-based method | Color sensor-based method | -Features are extracted from banknote images of various color channels [1,5]. -Detecting counterfeit Indian banknotes using features from HSV color space and neural network classifier [12]. | Using a single sensor causes simplicity in the algorithm and system implementation with reduced processing time | -Banknote images with multiple color channels must be acquired, and a large number of features based on many weak classifiers must be combined, thus reducing the processing speed [1,5]. -Mainly focuses on counterfeit banknote classification [12]. |
Gray sensor-based method | Chinese banknote classification using neural network based on the features of gray-level histogram [7] -Fitness classification based on DWT and SVM (proposed method) | -Fast image acquisition by single gray sensor with less memory usage | -Mainly focuses on banknote classification [7]. -Additional procedure for SVM training is required (proposed method) |
2. Proposed Method
2.1. Overview of the Proposed Method
2.2. ROI Cropping and Feature Extraction
2.3. Selection of Optimal Features Using Regression Analysis
2.4. SVM Training and Testing
3. Experimental Results
Denominations | A Direction | B Direction | C Direction | D Direction |
---|---|---|---|---|
10 Rupee | 1040 | 1020 | 1020 | 1020 |
20 Rupee | 680 | 670 | 710 | 710 |
50 Rupee | 620 | 620 | 650 | 650 |
100 Rupee | 1540 | 1550 | 1520 | 1530 |
500 Rupee | 930 | 910 | 950 | 960 |
Denom. | Dir. | Haar DWT | Daubechies DWT | ||||||
---|---|---|---|---|---|---|---|---|---|
Train 1—Test 2 | Train 2—Test 1 | Train 1—Test 2 | Train 2—Test 1 | ||||||
Selected Features | R2 | Selected Features | R2 | Selected Features | R2 | Selected Features | R2 | ||
10 Rupee | A | LL mean | 0.6909 | LL mean | 0.8266 | LL mean | 0.6833 | LL mean | 0.8327 |
LL std | 0.6437 | LL std | 0.7720 | LL std | 0.6365 | LL std | 0.7792 | ||
B | LL mean | 0.6654 | LL mean | 0.8443 | LL mean | 0.6812 | LL mean | 0.8284 | |
LL std | 0.6099 | LL std | 0.7455 | LL std | 0.6109 | LL std | 0.7477 | ||
C | LH std | 0.9026 | LH std | 0.9296 | LH std | 0.8888 | LH std | 0.9197 | |
LL mean | 0.8055 | HH std | 0.8691 | LL mean | 0.8176 | HL std | 0.8692 | ||
D | LH std | 0.9052 | LH std | 0.9274 | LH std | 0.8845 | LH std | 0.9164 | |
LL mean | 0.8300 | HH std | 0.8628 | LL mean | 0.8394 | HL std | 0.8587 | ||
20 Rupee | A | LL std | 0.7222 | LL std | 0.8243 | LL std | 0.7244 | LL std | 0.8238 |
LL mean | 0.5351 | LL mean | 0.6733 | LL mean | 0.5682 | LL mean | 0.6864 | ||
B | LL std | 0.7000 | LL std | 0.8075 | LL std | 0.6917 | LL std | 0.8239 | |
LL mean | 0.5791 | LL mean | 0.6760 | LL mean | 0.5799 | LL mean | 0.6746 | ||
C | LH std | 0.8287 | HL std | 0.7775 | LH std | 0.8034 | HL std | 0.7834 | |
HL std | 0.7781 | LH std | 0.7412 | HL std | 0.7783 | LH std | 0.7439 | ||
D | LH std | 0.8514 | LH std | 0.7314 | LH std | 0.8282 | LH std | 0.7526 | |
HL std | 0.7964 | LL mean | 0.7096 | HL std | 0.7962 | LL mean | 0.7105 | ||
50 Rupee | A | LL std | 0.9018 | LL std | 0.9249 | LL std | 0.9043 | LL std | 0.9224 |
LH std | 0.8949 | LL mean | 0.8764 | LL mean | 0.8526 | LL mean | 0.8887 | ||
B | LL std | 0.8934 | LL std | 0.9315 | LL std | 0.8960 | LL std | 0.9274 | |
LH std | 0.8778 | LL mean | 0.8762 | LL mean | 0.8557 | LL mean | 0.8817 | ||
C | LH std | 0.9611 | LH std | 0.9390 | LH std | 0.9511 | LH std | 0.9235 | |
LL mean | 0.9558 | HL std | 0.9144 | LL mean | 0.9471 | LL mean | 0.9087 | ||
D | LH std | 0.9627 | LH std | 0.9450 | LH std | 0.9518 | LL mean | 0.9414 | |
HL std | 0.9489 | LL mean | 0.9439 | LL mean | 0.9418 | LH std | 0.9374 | ||
100 Rupee | A | LH std | 0.8213 | LH std | 0.8307 | LH std | 0.7635 | LL std | 0.8234 |
LL mean | 0.7222 | LL std | 0.8146 | LL mean | 0.7210 | LH std | 0.7917 | ||
B | LH std | 0.8170 | LH std | 0.8249 | LL mean | 0.7160 | LL std | 0.8313 | |
LL mean | 0.7395 | LL mean | 0.8062 | LL std | 0.7141 | LL mean | 0.8007 | ||
C | LH std | 0.8599 | LH std | 0.8817 | LH std | 0.8276 | HL std | 0.8723 | |
HL std | 0.8171 | HL std | 0.8638 | HL std | 0.7986 | LH std | 0.8694 | ||
D | LH std | 0.8502 | LH std | 0.9030 | LH std | 0.8112 | LH std | 0.8476 | |
HL std | 0.8073 | HL std | 0.8858 | LL mean | 0.7883 | HL std | 0.8448 | ||
500 Rupee | A | LL std | 0.6582 | LL std | 0.5521 | LL std | 0.6448 | LL std | 0.5581 |
HL mean | 0.4041 | HL mean | 0.3839 | HL mean | 0.3582 | LL mean | 0.3580 | ||
B | LL std | 0.4907 | LL std | 0.5184 | LL std | 0.5108 | LL std | 0.5510 | |
HH std | 0.2833 | LL mean | 0.3015 | HL std | 0.2627 | LL mean | 0.3174 | ||
C | LH std | 0.9314 | LH std | 0.8388 | LH std | 0.9105 | LH std | 0.7695 | |
HL std | 0.9309 | HL std | 0.7307 | HL std | 0.8899 | LL mean | 0.6551 | ||
D | HL std | 0.9291 | LH std | 0.8523 | LH std | 0.9270 | LH std | 0.8016 | |
LH std | 0.9203 | HL std | 0.7639 | LL mean | 0.8959 | LL mean | 0.6472 |
Denom. | Dir. | SVM Kernel | Train 1—Test 2 | Train 2—Test 1 | Average EER | ||||
---|---|---|---|---|---|---|---|---|---|
Type 1 Error | Type 2 Error | EER | Type 1 Error | Type 2 Error | EER | ||||
10 Rupee | A | linear | 4.8889 | 0.0000 | 1.8841 | 0.0000 | 0.0000 | 0.0000 | 1.1764 |
B | sigmoid | 2.7273 | 0.0000 | 0.3448 | 0.0000 | 3.3333 | 0.5882 | 0.4575 | |
C | RBF | 2.0000 | 0.0000 | 0.4762 | 0.0000 | 1.6667 | 0.0000 | 0.2779 | |
D | RBF | 0.6667 | 3.3333 | 0.9524 | 0.0000 | 1.6667 | 0.0000 | 0.5555 | |
20 Rupee | A | RBF | 0.0000 | 0.0000 | 0.0000 | 1.2903 | 40.0000 | 2.2581 | 1.1290 |
B | RBF | 0.0000 | 5.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
C | RBF | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 35.0000 | 1.3514 | 0.7142 | |
D | linear | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
50 Rupee | A | RBF | 0.4545 | 16.6667 | 2.8947 | 2.0833 | 0.0000 | 0.0000 | 2.2385 |
B | linear | 0.4545 | 0.0000 | 0.4545 | 3.7500 | 0.0000 | 0.0000 | 0.2275 | |
C | linear | 0.4348 | 1.0000 | 0.3030 | 0.0000 | 0.0000 | 0.0000 | 0.1786 | |
D | RBF | 0.4348 | 0.0000 | 0.3030 | 2.1739 | 0.0000 | 0.4348 | 0.3573 | |
100 Rupee | A | linear | 0.0000 | 12.0000 | 0.9524 | 0.0000 | 12.0000 | 0.1333 | 0.5605 |
B | linear | 0.0000 | 4.0000 | 0.0000 | 0.8333 | 7.5000 | 2.0968 | 1.3266 | |
C | linear | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
D | linear | 0.2703 | 0.0000 | 0.0000 | 0.0000 | 23.3333 | 0.1316 | 0.0671 | |
500 Rupee | A | poly | 0.4651 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
B | sigmoid | 0.0000 | 50.0000 | 0.2381 | 1.1111 | 0.0000 | 0.0000 | 0.1190 | |
C | sigmoid | 2.9545 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
D | sigmoid | 3.1111 | 0.0000 | 0.0000 | 0.0000 | 3.3333 | 0.0000 | 0.0000 | |
Average EER | 0.4693 |
Denom. | Dir. | SVM Kernel | Train 1–Test 2 | Train 2–Test 1 | Average EER | ||||
---|---|---|---|---|---|---|---|---|---|
Type 1 Error | Type 2 Error | EER | Type 1 Error | Type 2 Error | EER | ||||
10 Rupee | A | linear | 4.2222 | 0.0000 | 0.3774 | 0.0000 | 1.6667 | 0.0000 | 0.2039 |
B | linear | 1.1364 | 0.0000 | 0.5882 | 0.0000 | 6.6667 | 0.1961 | 0.3944 | |
C | RBF | 2.6667 | 0.0000 | 1.1765 | 0.0000 | 1.6667 | 0.3509 | 0.7405 | |
D | sigmoid | 0.2222 | 8.3333 | 2.1569 | 0.6667 | 0.0000 | 0.0000 | 1.2499 | |
20 Rupee | A | sigmoid | 0.0000 | 0.0000 | 0.0000 | 1.2903 | 0.0000 | 1.2121 | 0.6249 |
B | sigmoid | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 30.0000 | 0.5882 | 0.3126 | |
C | sigmoid | 0.0000 | 0.0000 | 0.0000 | 0.3030 | 20.0000 | 0.3030 | 0.1515 | |
D | linear | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
50 Rupee | A | linear | 0.0000 | 0.0000 | 0.0000 | 0.8333 | 0.0000 | 0.0000 | 0.0000 |
B | linear | 0.4545 | 0.0000 | 0.0000 | 0.4167 | 0.0000 | 0.0000 | 0.0000 | |
C | linear | 0.0000 | 3.0000 | 0.0000 | 3.0435 | 0.0000 | 0.0000 | 0.0000 | |
D | sigmoid | 0.0000 | 0.0000 | 0.0000 | 1.3043 | 0.0000 | 0.4348 | 0.2175 | |
100 Rupee | A | linear | 0.0000 | 8.0000 | 0.5051 | 0.0000 | 8.0000 | 0.5556 | 0.5269 |
B | RBF | 0.4054 | 4.0000 | 1.2162 | 0.2778 | 5.0000 | 0.7500 | 0.9707 | |
C | linear | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 16.6667 | 0.0000 | 0.0000 | |
D | poly | 0.4054 | 0.0000 | 0.3822 | 0.0000 | 26.6667 | 1.1650 | 0.8290 | |
500 Rupee | A | linear | 0.4651 | 0.0000 | 0.0000 | 0.0000 | 3.3333 | 0.0000 | 0.0000 |
B | linear | 0.0000 | 25.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
C | linear | 2.9545 | 0.0000 | 0.6522 | 0.0000 | 13.3333 | 0.0000 | 0.3334 | |
D | RBF | 2.6667 | 0.0000 | 1.3115 | 0.0000 | 0.0000 | 0.0000 | 0.7548 | |
Average EER | 0.3655 |
Correct Classification | Type 1 Error Case | Type 2 Error Case | ||
---|---|---|---|---|
Fit case | Unfit case | |||
Cropped ROI | | | | |
Image by Haar DWT | | | | |
Denomination | Direction | Haar DWT | Daubechies DWT | Previous Method [7] |
---|---|---|---|---|
10 Rupee | A | 1.1764 | 0.2039 | 6.9036 |
B | 0.4575 | 0.3944 | 16.2962 | |
C | 0.2779 | 0.7405 | 6.2792 | |
D | 0.5555 | 1.2499 | 16.5487 | |
20 Rupee | A | 1.1290 | 0.6249 | 25.0000 |
B | 0.0000 | 0.3126 | 25.3456 | |
C | 0.7142 | 0.1515 | 26.7717 | |
D | 0.0000 | 0.0000 | 28.7490 | |
50 Rupee | A | 2.2385 | 0.0000 | 5.2397 |
B | 0.2275 | 0.0000 | 16.0191 | |
C | 0.1786 | 0.0000 | 2.8302 | |
D | 0.3573 | 0.2175 | 0.0000 | |
100 Rupee | A | 0.5605 | 0.5269 | 1.2179 |
B | 1.3266 | 0.9707 | 2.1053 | |
C | 0.0000 | 0.0000 | 0.6868 | |
D | 0.0671 | 0.8290 | 1.3765 | |
500 Rupee | A | 0.0000 | 0.0000 | 25.0000 |
B | 0.1190 | 0.0000 | 25.0000 | |
C | 0.0000 | 0.3334 | 0.0000 | |
D | 0.0000 | 0.7548 | 0.0000 | |
Average EER | 0.4693 | 0.3655 | 11.5685 |
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Share and Cite
Pham, T.D.; Park, Y.H.; Kwon, S.Y.; Nguyen, D.T.; Vokhidov, H.; Park, K.R.; Jeong, D.S.; Yoon, S. Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor. Sensors 2015, 15, 21016-21032. https://doi.org/10.3390/s150921016
Pham TD, Park YH, Kwon SY, Nguyen DT, Vokhidov H, Park KR, Jeong DS, Yoon S. Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor. Sensors. 2015; 15(9):21016-21032. https://doi.org/10.3390/s150921016
Chicago/Turabian StylePham, Tuyen Danh, Young Ho Park, Seung Yong Kwon, Dat Tien Nguyen, Husan Vokhidov, Kang Ryoung Park, Dae Sik Jeong, and Sungsoo Yoon. 2015. "Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor" Sensors 15, no. 9: 21016-21032. https://doi.org/10.3390/s150921016
APA StylePham, T. D., Park, Y. H., Kwon, S. Y., Nguyen, D. T., Vokhidov, H., Park, K. R., Jeong, D. S., & Yoon, S. (2015). Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor. Sensors, 15(9), 21016-21032. https://doi.org/10.3390/s150921016