Proceedings ArticleDOI
Identity Determination with Offline Handwritten Input Using Multi Kernel Feature Combination
Ehtesham Hassan,Santanu Chaudhury,M. Gopal +2 more
- pp 84-88
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TLDR
A novel framework for combining the features for identification is presented and combines the features in kernel space in MKL based framework for writer recognition and signature verification.Abstract:
The paper presents three novel features for handwritten data based identity recognition. A novel framework for combining the features for identification is presented. The framework combines the features in kernel space in MKL based framework. The application of features individually and in combination is presented for writer recognition and signature verification. The writer recognition results have been presented for Devanagari script input and signature verification results have been presented for open dataset [1]. The experiments have shown encouraging results.read more
Citations
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Proceedings ArticleDOI
The current state of art: handwriting a behavioral biometric for person identification and verification
Sharada Laxman Kore,Shaila Apte +1 more
TL;DR: A comparative study is presented for text dependent and text independent methods for off line cursive and isolated English handwriting up till date.
Journal ArticleDOI
Off-line hand written input based identity determination using multi kernel feature combination
TL;DR: A scheme for multiple feature based identity establishment using multi-kernel learning using genetic algorithm and the efficacy of the framework using individual and combination of features is demonstrated for Devanagari script input.
Proceedings ArticleDOI
Geometrical feature based ranking using grey relational analysis (GRA) for writer identification
TL;DR: This study presents the Higher-Order United Moment Invariant (HUMI) as the global feature extraction methods and demonstrates that the average classification accuracy of five classifiers by using just the combination of two most significant features have yielded a better performance than using all features.
References
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Journal ArticleDOI
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TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Proceedings ArticleDOI
On feature combination for multiclass object classification
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Proceedings ArticleDOI
More efficiency in multiple kernel learning
TL;DR: This paper proposes an algorithm for solving the MKL problem through an adaptive 2-norm regularization formulation and provides an new insight on MKL algorithms based on block 1- norm regularization by showing that the two approaches are equivalent.
Journal ArticleDOI
Signature verification using multiple neural classifiers
Reena Bajaj,Santanu Chaudhury +1 more
TL;DR: Experimental results show that combination of the classifiers increases reliability of the recognition results and is the unique feature of this work.
Journal ArticleDOI
Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers
TL;DR: A new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature by using Bezier curves and then extracting features from these curves to improve the reliability of the classification.
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