Modified Dynamic Time Warping for Stroke-Based On-line Signature Verification

Chang, Won-Du; Shin, Jungpil

doi:10.1109/icdar.2007.4377010

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…DTW is used mostly in finite vocabulary speech recognition to handle different speaking speeds [ 63 , 64 ]. Besides speech recognition, DTW has been used for word spotting in handwritten historical documents on electronic media [ 65 ] and machine-printed documents [ 66 ], in signature [ 67 , 68 ] and gait recognition [ 69 ], for ECG signal classification [ 70 – 72 ], for fingerprint verification [ 73 ], and for face localization in color images [ 74 ]. In this study, DTW is used for classifying feature vectors extracted from gyroscope signals.…”

Section: Classification Techniquesmentioning

confidence: 99%

Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

Tunçel

Altun

Barshan

2009

Sensors

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This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost.

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Section: Classification Techniquesmentioning

confidence: 99%

Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

Tunçel

Altun

Barshan

2009

Sensors

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“…Going deeper into this problem, it is 57 hard to define what is a good forgery. Some works and related databases only exploit 58 forgeries of the image of the signature while in an online context [12,22,16,4]; few 59 exploit forgeries of the personal gesture of signing, additionally to skilled forgeries 60 of the image of the signature [5,6,36,2]. Finally, some works only exploit random 61 forgeries to evaluate the capacity of systems to discriminate forgeries from genuine 62 signatures [18].…”

mentioning

confidence: 99%

“…On 175 one hand, in [35], the fusion of three elastic distance measures each resulting from 176 matching two signatures at a given level of description, is performed. On the other 177 hand, systems performing the alignment at the stroke level have also appeared [4], 178 reducing the computational load of the matching process. Such systems are de-179 scribed in the following paragraph.…”

mentioning

confidence: 99%

“…On the other hand, Chang et al [4] proposed a stroke-based signature verification 187 method based on Dynamic Time Warping and tested the method on Japanese sig-188 natures. It is interesting to note that in Asian signatures, stroke information may be 189 indeed more representative than in Western signatures in which intra-stroke varia-190 tion is more pronounced.…”

mentioning

confidence: 99%

“…On 600 a particular test signature, a distance is computed between its log-likelihood and 601 (x g , y g ) of the signature t1. 4 3 Speed in x t1. 5 4 Speed in y t1.…”

mentioning

confidence: 99%

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