Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures. In this paper, we suggest a novel method for online signature verification that relies on the increasingly available hand-worn devices, such as smartwatches or fitness trackers, instead of dedicated ad-hoc devices. Our method uses a set of known genuine and forged signatures, recorded using the motion sensors of a hand-worn device, to train a machine learning classifier. Then, given the recording of an unknown signature and a claimed identity, the classifier can determine whether the signature is genuine or forged. In order to validate our method, it was applied on 1980 recordings of genuine and forged signatures that we collected from 66 subjects in our institution. Using our method, we were able to successfully distinguish between genuine and forged signatures with a high degree of accuracy (0.98 AUC and 0.05 EER).
In contrast, online signature verification methods take the dynamic writing process into account @cite_21 . A signature is represented by a pen tip trajectory measurement that captures the position of the pen over time; depending on the digitizer, this may be accompanied by additional measurements of the pressure and pen inclination. In this case, the signature is represented as a sequence @math ; @math , where @math is the signal sampled at time @math and @math is the sampling interval @cite_10 . Clearly, the additional time dimension captured by online methods provides valuable information about the signature, leading to a higher level of verification performance in general @cite_14 .
@cite_14 @cite_21 @cite_10
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In online handwriting recognition the trajectory of the pen is recorded during writing. Although the trajectory provides a compact and complete representation of the written output, it is hard to transcribe directly, because each letter is spread over many pen locations. Most recognition systems therefore employ sophisticated preprocessing techniques to put the inputs into a more localised form. However these techniques require considerable human effort, and are specific to particular languages and alphabets. This paper describes a system capable of directly transcribing raw online handwriting data. The system consists of an advanced recurrent neural network with an output layer designed for sequence labelling, combined with a probabilistic language model. In experiments on an unconstrained online database, we record excellent results using either raw or preprocessed data, well outperforming a state-of-the-art HMM based system in both cases. This paper describes an algorithm for verification of signatures written on a pen-input tablet. The algorithm is based on a novel, artificial neural network, called a "Siamese" neural network. This network consists of two identical sub-networks joined at their outputs. During training the two sub-networks extract features from two signatures, while the joining neuron measures the distance between the two feature vectors. Verification consists of comparing an extracted feature vector with a stored feature vector for the signer. Signatures closer to this stored representation than a chosen threshold are accepted, all other signatures are rejected as forgeries. This paper deals with the detection of freehand forgeries of signatures on bank checks. The detection process makes use of size ratio and slant features derived from Eden's kinematic stroke model for handwriting, which was modified to make it applicable to prewritten material. The features are measured for a real signature by a process involving automatic thresholding, to extract the signature from the background; analysis of projections, to segment the signature into vertical zones; detection of tall letters, to segment it into horizontal zones; and identification of the tall letters with respect to the (assumed known) spelling of the signature. Statistical assumptions are made regarding the expected variation in feature values among different writers and for a single writer. Tests on a small data base led to verification of these assumptions and to successful forgery detection. This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract signature features from the input pen-tip trajectory, a fully convolutional network to learn informative representation, a sequence modeling layer to make per-frame predictions on the input sequence and a transcription layer to translate the predictions into a label sequence. We also present a refined beam search method that efficiently integrates the language model to decode the FCRN and significantly improve the recognition results. We evaluate the performance of the proposed method on the test sets from the databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition competition, and both achieve state-of-the-art performance with correct rates of 96.40 and 95.00 , respectively.