Signature recognition

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

Are Haar-like Rectangular Features for Biometric Recognition Reducible?

Abstract: Biometric recognition is still a very difficult task in real-world scenarios wherein unforeseen changes in degradations factors like noise, occlusion, blurriness and illumination can drastically affect the extracted features from the biometric signals. Very recently Haar-like rectangular features which have usually been used for object detection were introduced for biometric recognition resulting in systems that are robust against most of the mentioned degradations [9]. The problem with these features is that one can define many different such features for a given biometric signal and it is not clear whether all of these features are required for the actual recognition or not. This is exactly what we are dealing with in this paper: How can an initial set of Haar-like rectangular features, that have been used for biometric recognition, be reduced to a set of most influential features? This paper proposes total sensitivity analysis about the mean for this purpose for two different biometric traits, iris and face. Experimental results on multiple public databases show the superiority of the proposed system, using the found influential features, compared to state-of-the-art biometric recognition systems.

Signature Recognition Based on Discrete Wavelet Transform

Abstract: Personal identification is an actively developing area of research. Human signature is a vital biometric attribute which can be used to authenticate human identity. There are many approaches to recognize signature with a lot of researches. The aim of this research is to introduce an efficient approach for signature recognition. This approach starts with the process the acquired signatures and stores these signatures in the database to be ready for verification. The collection of signature data based on collecting samples of 10 people and 10 signatures for each person through traditional ink stamp method. These signatures are digitized to be ready for processing. Many steps are applied to the acquired images to perform the pre-processing stage. The proposed approach based on discrete wavelet transforms to extract significant features from each signature image. Pre-processing is applied at the beginning of this approach to avoid any unwanted noise. This approach consists of many steps: Data acquisition, pre-processing, signature registration, and feature extraction. High recognition rate results (100%) are obtained through applying this approach.

Segmentation-Free Biometric Recognition Using Correlation Filters

Abstract: In most biometric recognition studies, test biometric signatures (e.g., faces, irises, finger prints, etc.) are segmented from their background before they are compared to stored signatures. However, such segmentation is not easy to carry out in challenging imaging conditions. Here we show that correlation filters (CFs) can be used to avoid segmentation and achieve segmentation-free biometric recognition. CFs do not require the object of interest to first be localized or segmented. In this paper we review in detail the most popular CF design algorithms and discuss their different usages and advantages. We begin with still images and then explore their usage in image sequences or video and activity recognition. As an example of the power of CFs, experimental result are presented in this paper is in the area of recognizing people in videos using their ocular (eye) regions where common iris recognition techniques fails due to low resolution. We also discuss examples when CFs have been applied to recognize faces, to localize pedestrians, and to recognize pedestrian actions.

GMM for offline signature forgery detection

Abstract: As signature continues to play a crucial part in personal identification for number of applications including financial transaction, an efficient signature authentication system becomes more and more important. Various researches in the field of signature authentication has been dynamically pursued for many years and its extent is still being explored. Signature verification is the process which is carried out to determine whether a given signature is genuine or forged. It can be distinguished into two types such as the Online and the Offline. In this paper we presented the Offline signature verification system and extracted some new local and geometric features like QuadSurface feature, Area ratio, Distance ratio etc. For this we have taken some genuine signatures from 5 different persons and extracted the features from all of the samples after proper preprocessing steps. The training phase uses Gaussian Mixture Model (GMM) technique to obtain a reference model for each signature sample of a particular user. By computing Euclidian distance between reference signature and all the training sets of signatures, acceptance range is defined. If the Euclidian distance of a query signature is within the acceptance range then it is detected as an authenticated signature else, a forged signature.