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The dynamic signature is a biometric trait widely used and accepted for verifying a person’s identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person’s signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual’s identity. To mitigate this drawback, this paper proposes a procedure...
This research proposed an automatic student identification and verification system utilising off-line Thai name components. The Thai name components consist of first and last names. Dense texture-based feature descriptors were able to yield encouraging results when applied to different handwritten text recognition scenarios. As a result, the authors employed such features in investigating their performance...
Standard Hidden Markov Models (HMM) have proved to be a very useful tool for temporal sequence pattern recognition, although they present a poor discriminative power. On the contrary Neural Networks (NNs) have been recognized as powerful tools for classification task, but they are less efficient to model temporal variation than HMM. In order to get the advantages of both HMMs and NNs, different hybrid...
The handwriting signature is one of the most popular behavioral biometric traits for person recognition. Such recognition systems capture the personal signing behaviour and its variability based on a limited number of enrolled signatures. In this paper a cognitive inspired model based on motor equivalence theory is developed to duplicate off-line signatures from one real on-line seed. This model achieves...
In this paper we present a biometric approach, based on lip shape. We have performed an image preprocessing, in order to detect the face of a person image. After this, we have enhanced the lips image using a color transformation, and next we do its detection. The parameterization is based on lips contour points. Those points have been transformed by a Hidden Markov Model (HMM) kernel, using a minimization...
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