Review the state of art works of handwritten signature verification works.
Depending on the acquisition method, signature verification systems are divided into: online (dynamic) and offline (static) with Writer Dependent or not categories.
- In online case, an acquisition device, such as a digitizing table, is used to acquire the user’s signature. The data is collected as a sequence over time, containing the position of the pen, and in some cases including additional information such as the pen inclination\velocity\angle\curvature\full acceleration\coordinates\pressure 倾角\速度\角度\曲率\全加速度\坐标\压力
- In offline signature verification, the signature is acquired after the writing process is completed. In this case, the signature is represented as a digital image or picture
- Also, the can be devided into writer independent and writer dependent classification cases
- SVM (Support Vector Machines) model is one of the most effective classifier for signature verification
- Texture features (especially Local Binary Patterns LBP) is the best hand-crafted feature extractors in signature verification
In many cases, the open source projects were deployed by matlab or python.
Hafemann, L. G., Sabourin, R., & Oliveira, L. S. (2017, November).
[1] Offline handwritten signature verification—literature review
URL: https://arxiv.org/pdf/1507.07909.pdf
[2] Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
URL: http://dx.doi.org/10.1016/j.patcog.2017.05.012 (preprint)
[3] Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
URL: https://doi.org/10.1007/s10032-018-0301-6 (preprint)
- Luizgh Offline Expert!
- Project site: https://www.etsmtl.ca/Unites-de-recherche/LIVIA/Recherche-et-innovation/Projets/Signature-Verification
- DCNN Deep convolutional neural network
- SigNet adopted as the feature approach
- wget "https://storage.googleapis.com/luizgh-datasets/models/signet_models.zip"
- 3 signet models: Refere to paper [2]
- signet.pkl; signetf_lambda0.95.pkl; and signetf_lambda0.999.pkl
- Input scaned signatures need to be preprocess_signature() before the CNN. The aim is to make sure the input image/signature under the same resolution by wrap/crop/resize functions
- 3 signet models: Refere to paper [2]
- wget "https://storage.googleapis.com/luizgh-datasets/models/signet_spp_models.zip"
- 4 signet models: Refere to paper [3]
- signet_spp_300dpi.pkl; signet_spp_300dpi_f.pkl and 600_dpi
- (with or without f ) Training the model with or without forgeries
- 300 or 600 is the scaned resolution of input image
- 300dpi: input_size: (428, 612) and image_size: (476, 680)
- 600dpi: input_size: (856, 1224) and image_size: (952, 1360)
- Improve the preprocess_signature() by the introduced SPP methodology.
- SPP the final layer in CNN, make sure CNN can handle any scale/size input image and generate the same sized feature vectors
- 4 signet models: Refere to paper [3]
- wget "https://storage.googleapis.com/luizgh-datasets/models/signet_models.zip"
- Be aware:
- Refer interactive_example.ipynb for SPP method
- the input signature image picture is 1 color channel file gray color rather than RGB.
- The time consumption during compile is high due to the limitation of Theano of DNN platform
- *****questions need to be confirmed *****
- Check the Euclidean Distance threshold in the paper (interactive_example.ipynb)
- For actual classification, the simplest approach is computing the distance (in feature space) between a query signature and a reference. This is easy to implement (see the jupyter notebook example), but not very powerful.
- Most commonly, people train Writer-dependent classifiers (one binary classifier for each user).
- Check the Euclidean Distance threshold in the paper (interactive_example.ipynb)
- https://github.com/EB324/signature_verification
- https://github.com/Malkhan52/Offline-Signature-Recognition
- https://github.com/Aftaab99/OfflineSignatureVerification
- https://github.com/ASoleimaniB/DMML UTSig DataBase author!
- https://github.com/jeongmincha/Online-Signature-Verification
- https://github.com/ThompZon/online-signature-verification
- https://github.com/kin3tik/Verifier
- With project report at: https://github.com/kin3tik/Verifier/blob/master/doc/Project%20Report.pdf
- https://github.com/DonatoMeoli/DS-SRS
- With project paper at: https://github.com/DonatoMeoli/DS-SRS/blob/master/paper.pdf
- https://github.com/ethanyxfang/MDB-DTW-for-signature-verification
Online Signature Verification on Mobile Devices
- 2014 by Nasir D. Memon (Hey prof, long time no see, how are you?)
- https://www.researchgate.net/publication/262055983_Online_Signature_Verification_on_Mobile_Devices
- 2016 by Prof.Jadhav Hemant B (Read the conclusion suggested.)
- http://www.ijste.org/articles/IJSTEV2I10172.pdf
- offline: UTSig (University of Tehran Offline Signature Dataset)
- scanned png files
- URL: https://sites.google.com/site/amirsoleimanibajestani/publication