6,712 research outputs found
Signature Verification Approach using Fusion of Hybrid Texture Features
In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class support vector machines (SVMs) corresponding to each set of
LQP and Wavelet features are trained to obtain two different authenticity
scores for a given signature. Finally, a score level classifier fusion method
is used to integrate the scores obtained from the two one-class SVMs to achieve
the verification score. In the proposed method only genuine signatures are used
to train the one-class SVMs. The proposed signature verification method has
been tested using four different publicly available datasets and the results
demonstrate the generality of the proposed method. The proposed system
outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
Offline signature verification using classifier combination of HOG and LBP features
We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are
calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors
of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent
SVMs are separately trained for each subject using genuine and random forgeries.
The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries
in training
Use of ERTS-1 data to access and monitor change in the west side of the San Joaquin Valley and central coastal zone of California
There are no author-identified significant results in this report
Freeman chain code as representation in offline signature verification system
Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features
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