Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

Abstract: Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.

A Two-Level Security System Based on Multimodal Biometrics and Modified Fusion Technique

Abstract: Biometrics is physical or behavioral characteristics to identify a human digitally. The identification of humans involves training and testing process. In general, the training process requires a large database to store the extracted features of multiple samples of a human, and the lighting conditions of acquiring samples also make it more complex. These challenges of using single biometric can be addressed by using multimodal biometrics. In this paper, multimodal biometrics of humans like face, fingerprint, and iris are considered for their easy and secure recognition among the group of people. The testing process involves the comparison of the features of real-time sample with the features of training samples, and consequently, the recognition rate of this algorithm increases. This algorithm uses discrete wavelet transform (DWT) for the filtering and compression, singular value decomposition (SVD) for the extraction of features from the compressed and filtered results of DWT. The proposed method gives better performance like 98% of accuracy and 100% recognition rate.

Study of Zone-Based Feature for Online Handwritten Signature Recognition and Verification in Devanagari Script

Abstract: This paper presents one zone-based feature extraction approach for online handwritten signature recognition and verification of one of the major Indic scripts–Devanagari. To the best of our knowledge no work is available for signature recognition and verification in Indic scripts. Here, the entire online image is divided into a number of local zones. In this approach, named Zone wise Slopes of Dominant Points (ZSDP), the dominant points are detected first from each stroke and next the slope angles between consecutive dominant points are calculated and features are extracted in these local zones. Next, these features are supplied to two different classifiers; Hidden Markov Model (HMM) and Support Vector Machine (SVM) for recognition and verification of signatures. An exhaustive experiment in a large dataset is performed using this zone-based feature on original and forged signatures in Devanagari script and encouraging results are found.

Online handwriting Farsi character and number recognition based on hand movement direction using Hidden Markov Models

Abstract: Online handwriting recognition has many applications and the recognition with high accuracy is essential. In this paper, we introduce a method for online handwriting Farsi character and number recognition using Hidden Markov Models (HMM). First we recognize handwriting direction then we get some statistical and formatting features. The letters are classified by means of these features and then we use HMM for the recognition. We have some movements outside of the main body at the beginning or the end letters and number, so in this paper we put a Noise State at the beginning and end hmm and put an accepting state at the end to increase the recognition accuracy. We use Baum-Welch algorithm to introduce HMM and then give some samples. Note that the test results demonstrate the scalability of the proposed model and the recognition accuracy for numbers is 99.22% and for letters is 95.91%.

Offline handwritten signature recognition using polar-scale normalization

Abstract: Offline handwritten signature is still widely used for person verification in financial and business transactions. Most research in offline handwritten signature at-tempts to improve feature extraction and classification for the better recognition rate. The deformation and unsteadiness of handwritten signatures, such as direction, declination, and size, are also the key factors sensitive to recognition rate. Therefore, this paper focuses on the pre-processing phase, which is an alternative way to improve the accuracy and to make such factors stable. This study is based on the hypothesis; a table signature size is able to boost up the recognition rate. Therefore, polar-scale normalization (PSN) is proposed to scale signature size and make it stable. In this method, the signature images are transformed into the polar coordinate system consisting of polar distance and angle, and then normalized by ‖norm‖. The normalized distance is certainly estimated by polar coordinate that helps reduce the deformed images. The 5,739-sample signature images with 150 classes are used to test in the experiment. PSN provide the better performance, when compared with traditional normalization schemes including min-max, decimal, z-score and MAD normalizations. The results reveal that the proposed method can improve the average recognition rate up to 98.39%.