Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network
<p>Wavelet packet at depth 3. <span class="html-italic">N<sub>level,sequence</sub></span> is the WP node.</p> "> Figure 2
<p>DWT-tree by paramedic Mallat’s algorithm. Where down-arrow with number 2 symbol denotes the decimation (dyaddown) operation.</p> "> Figure 3
<p>Shows WPE calculated for WP at depth 5 for two persons. (<b>a</b>)The feature extraction vector for three signatures of person 1; (<b>b</b>) The feature extraction vector for three signatures of person 2.</p> "> Figure 4
<p>Flowchart of the proposed system.</p> "> Figure 5
<p>The original probabilistic neural network Structure.</p> "> Figure 6
<p>Radial basis transfer function.</p> "> Figure 7
<p>Competitive transfer function.</p> "> Figure 8
<p>The verification rate results with regard to GTS and FTS for Threshold, Log energy, and Shannon. (<b>a</b>) WPENN; (<b>b</b>) DWENN. WP and DWT at level five and bior2.2 wavelet function were used.</p> "> Figure 9
<p>The verification rate results for comparison between several feature extraction methods.</p> ">
Abstract
:1. Introduction
- Feature-based approaches, in which a holistic vector, consisting of global features such as signature duration, standard deviation, etc., is derived from the acquired signature trajectories.
- Function-based approaches, in which time sequences describing local properties, such as position trajectory, pressure, and azimuth, are used. A system for verifying handwritten signatures where various static and dynamic signature features are extracted and used as a pattern to train several network topologies is presented [11]. A signature verification system based on a Hidden Markov Model approach for verifying the hand signature data is presented in [12]. Instrumented data gloves furnished with sensors for detecting finger bend, hand position, and orientation for detecting hand signatures are used in handwritten verification [13]. A method for automatic handwritten signature verification that depends on global features that summarize different aspects of signature shape and dynamics of signature production is studied in [14]. A signature recognition algorithm, relying on a pixel-to-pixel relationship between signature images based on extensive statistical examination, standard deviation, variance, and theory of cross-correlation, is investigated in [15]. Online reference data acquired through a digitizing tablet is used with three different classification schemes to recognize handwritten signatures, as discussed in [16]. The influence of an incremental level of skill in the forgeries against signature verification systems is clarified in [17]. Principles for an improved writer enrollment based on an entropy measure for genuine signatures is presented in [18]. Dynamic signature verification systems, using a set of 49 normalized features that tolerate inconsistencies in genuine signatures while retaining the power to discriminate against forgeries, are studied in [19]. A statistical quantization mechanism to suppress the intra-class variation in signature features and statistical quantization mechanism, thus distinguishing the difference between genuine signature and its forgery, is emphasized in [20]. This method is not well-established in the field of signature recognition and needs to be analyzed based on several databases. Other signature verification systems based on extracting local information time functions of various dynamic properties of the signatures are used for comparison. The use of a discrete wavelet transform (DWT) in extracting features from handwritten signatures that achieved higher verification rates than that of a time domain verification system is reported in [21,22,23]. The limitation of 1D DWT is that it generates discrete wavelet sub-signals from the approximation first level sub-signal. Therefore, the DWT representation could lose the high-frequency components.
- (1)
- For the wavelet, the crucial feature of a signature is the signature morphology, as well as the lines concentration [35]. Bearing in mind this fact, the use of wavelet transform entropy would benefit immensely in features tracking, because of the possibility of signal analysis over several passbands of frequency.
- (2)
- For PNN, the feature vector is relatively not long, and that would not affect the PNN algorithm computational complexity. On the other hand, the possibility of working in an embedded training mode makes the system works online. This is easier for implementation, as well as giving PNN the ability to provide the confidence in its decision that follows directly from the Bayes’ theorem [36,37]. Although this process doesn’t affect the system’s performance, it will offer a speedy process as well as perform in a very timely manner.
2. Wavelet Transform Entropy for Feature Extraction
3. Wavelet Transform for Signature Feature Extraction
3.1. Wavelet Packet Transform
3.2. Discrete Wavelet Transform
- −
- Adaptive time-frequency windows;
- −
- Lower aliasing data deformation for one and two-dimensional signal processing applications;
- −
- Low computational complexity (O(N)), where N is the length of data; and
- −
- Inherent scalability.
3.3. Feature Extraction Procedure
- (1)
- Varying widely from class to class;
- (2)
- Stable over a long period of time;
- (3)
- Should not have a correlation with other features.
4. Classification
5. Results and Discussion
- The performance of identification rates WPENN is slightly better than that achieved with DWENN. That why it will be chosen for the final system.
- Threshold entropy results outperformed other used entropies.
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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DWENN by Shannon Entropy | ||||
Db1 | Db2 | Db3 | Db6 | Db10 |
79.44 | 77.44 | 77.88 | 79.88 | 80.08 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
79.44 | 79.44 | 79.44 | 80.08 | 81.66 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
79.44 | 78.23 | 81.11 | 83.88 | 81.11 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
80 | 81.66 | 80.55 | 82.77 | 81.11 |
DWENN by Log Energy Entropy | ||||
Db1 | Db2 | Db3 | Db6 | Db10 |
66.44 | 72.77 | 67.22 | 81.05 | 80.11 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
67.22 | 72.77 | 67.22 | 72.77 | 81.88 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
67.22 | 70 | 68.44 | 62.22 | 70 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
69.44 | 80.55 | 80 | 76.11 | 78.33 |
DWENN by Threshold (, ) Entropy | ||||
Db1 | Db2 | Db3 | Db6 | Db10 |
86.11 | 84.88 | 84.22 | 86.66 | 89.99 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
86.11 | 84.88 | 84.88 | 86.11 | 86.11 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
86.11 | 83.44 | 82.44 | 86.11 | 85 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
85.55 | 85.55 | 83.33 | 86.66 | 86.88 |
WPENN by Shannon Entropy | ||||
Db1 | Db2 | Db3 | Db6 | Db10 |
79.88 | 79.44 | 81.44 | 80.22 | 78.88 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
79.88 | 79.44 | 81.11 | 81.44 | 82.77 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
79.88 | 80.55 | 82.22 | 81.11 | 80 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
82.33 | 80.11 | 80.11 | 80.11 | 81.88 |
WPENN by Log Energy | ||||
Db1 | Db2 | Db3 | Db6 | Db10 |
43.33 | 80.55 | 77.22 | 83.33 | 81.11 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
43.33 | 80.55 | 77.22 | 83.33 | 84.44 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
43.33 | 65 | 73.33 | 61.66 | 83.33 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
81.11 | 83.88 | 86.66 | 81.11 | 79.44 |
WPENN by Threshold (, ) Entropy | ||||
Db1 | Db2 | Db3 | Db6 | Db10 |
86.1 | 87.77 | 88.88 | 88.33 | 86.10 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
86.1 | 87.03 | 88.23 | 85.55 | 89.88 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
86.1 | 85.22 | 86.11 | 87.88 | 85.55 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
87.88 | 83.88 | 85.55 | 87.77 | 92.06 |
WPENN by Threshold (P1 = 175, P2 = 10) Entropy/Fraud | ||||
---|---|---|---|---|
Db1 | Db2 | Db3 | Db6 | Db10 |
46.66 | 48.33 | 46.66 | 46.66 | 46.11 |
Sym1 | Sym2 | Sym3 | Sym6 | Sym10 |
46.66 | 48.33 | 46.66 | 52.22 | 51.11 |
Bior1.1 | Bior1.3 | Bior1.5 | Bior2.2 | Bior3.5 |
46.66 | 50 | 49.44 | 47.22 | 50.55 |
Coif1 | Coif2 | Coif3 | Coif4 | Coif5 |
48.33 | 52.22 | 51.66 | 51.11 | 52.22 |
Method | 15/9 | 10/14 | First 5/Random 5 |
---|---|---|---|
WPENN | 92.06 | 87.12 | 87.89 |
Method | WPENN | WPESVM | DWENN | DWESVM | Average | ||||
---|---|---|---|---|---|---|---|---|---|
Wavelet Function | GTS | FTS | GTS | FTS | GTS | FTS | GTS | FTS | |
Db1 | 80.00 | 72.86 | 74.12 | 72.85 | 85.14 | 71.43 | 77.14 | 71.43 | 75.62 |
Sym6 | 85.71 | 71.43 | 82.00 | 65.70 | 82.80 | 65.71 | 80.00 | 65.71 | 74.88 |
Bior2.2 | 87.14 | 74.29 | 82.72 | 74.29 | 88.71 | 68.57 | 74.29 | 72.86 | 77.85 |
Coif1 | 85.73 | 65.70 | 78.57 | 70.00 | 84.29 | 71.48 | 74.28 | 75.78 | 75.72 |
Method | WPENN | WPESVM | DWENN | DWESVM | Average | ||||
---|---|---|---|---|---|---|---|---|---|
Wavelet Function | GTS | FTS | GTS | FTS | GTS | FTS | GTS | FTS | |
Level2 | 77.14 | 74.29 | 72.87 | 75.29 | 80.00 | 71.43 | 68.00 | 68.00 | 73.37 |
Level3 | 82.86 | 75.71 | 84.71 | 74.29 | 81.43 | 71.43 | 71.43 | 71.43 | 76.66 |
Level4 | 87.14 | 71.43 | 82.86 | 71.43 | 87.14 | 70.00 | 74.29 | 75.71 | 77.50 |
Level5 | 87.14 | 74.29 | 82.72 | 74.29 | 88.71 | 68.57 | 74.29 | 72.86 | 77.85 |
Level6 | 84.29 | 68.57 | 78.57 | 74.29 | 87.14 | 70.00 | 84.29 | 68.57 | 76.96 |
Rec. Method | Testing/Training | Length of the Feature Vector | Recognition Rate (%) | Confidence Interval 95% | |
---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||
RGMM (5 GMMs) [50] | 9/15 | 250 | 87.94 | 84.12 | 90.08 |
RCGMM (5 GMMs) [50] | 9/15 | 330 | 88.64 | 84.57 | 92.12 |
FFTGMM [53] | 9/15 | 256 | 56.51 | 50.89 | 60.7 |
RPNN [54] | 9/15 | 250 | 59.7 | 57.67 | 60.56 |
RCPNN [54,55] | 9/15 | 330 | 60.5 | 52.23 | 62.25 |
FFTPNN [47] | 9/15 | 256 | 60.6 | 55.34 | 68.78 |
DWEGMM (7 GMMs) [53,56] | 9/15 | 450 | 65.56 | 60.4 | 70.54 |
WPEGMM (2 GMMs) [55,56] | 9/15 | 2500 | 68.84 | 64.59 | 74.23 |
DWENN | 9/15 | 450 | 89.99 | 85.23 | 92.55 |
WPENN | 9/15 | 2500 | 92.06 | 90.34 | 94.24 |
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Daqrouq, K.; Sweidan, H.; Balamesh, A.; Ajour, M.N. Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network. Entropy 2017, 19, 252. https://doi.org/10.3390/e19060252
Daqrouq K, Sweidan H, Balamesh A, Ajour MN. Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network. Entropy. 2017; 19(6):252. https://doi.org/10.3390/e19060252
Chicago/Turabian StyleDaqrouq, Khaled, Husam Sweidan, Ahmad Balamesh, and Mohammed N. Ajour. 2017. "Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network" Entropy 19, no. 6: 252. https://doi.org/10.3390/e19060252