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Recent advancement in genomics technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. In this work, we have presented efficient contour aware segmentation approach based based on fully conventional network whereas for classification we have used extreme machine learning based on CNN features extracted from...
The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively...
The rate of data generation is enormously growing due to the number of internet users and its speed. This increases the possibility of intrusions causing serious financial damage. Detecting the intruders in such high-speed data networks is a challenging task. Therefore, in this paper, we present a high-speed Intrusion Detection System (IDS), capable of working in Big Data environment. The system design...
In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity...
This paper focus to analyze several fusion rule at matching score level to combine important features extracted from gait sequence images for human identification system. Gait sequence image is a non-stationary data and can be modelled using a statistical learning technique. The propose technique consists of three different stages. The pre-processing stage computes the average silhouette images to...
Biometric traits such as an iris texture is one of the dependable physiological biometric traits because of its uniqueness. In this paper, we explore a different approach of matching score fusion and the effect of normalization method to the fusion process. Despite a plenty of work of iris recognition methods have been proposed in recent years, many are paying attention to the feature extraction process...
In this paper, the Extended Morphological Profile with duality is proposed and studied for hyperspectral image classification, by which, the shape noise is reduced and thus better classification accuracy is obtained compared to the conventional Extended Morphological Profile technique. Moreover, the integration of a linear filtering technique and Support Vector Machine based classifier is also used...
The purpose of writing this paper is two-fold. First, it presents a novel signature stability analysis based on signature's local / part-based features. The Speeded Up Local features (SURF) are used for local analysis which give various clues about the potential areas from whom the features should be exclusively considered while performing signature verification. Second, based on the results of the...
This paper reviews several information fusion techniques and strategies in the application of multimodal biometrics system using face and palmprint images. Multimodal biometric is able to overcome several limitations in single modal biometric such as intra-class variations, less discriminative power, noise data and redundant features. By consolidating two kinds of modality a better performance can...
The information fusion of face and palmprint biometrics using local features is investigated at feature level. The proposed method uses local information extracted from local region of biometric image which has rich statistical information. The texture of each region is processed using multiresolution analysis with different orientations and scales. The feature dimensionality of each region is reduced...
This paper presents a novel signature verification system based on local features of signatures. The proposed system uses Fast Retina Key points (FREAK) which represent local features and are inspired by the human visual system, particularly the retina. To locate local points of interest in signatures, two local key point detectors, i.e., Features from Accelerated Segment Test (FAST) and Speeded-up...
This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures, and handwritten text) are considered where training...
The purpose of writing this paper is three-fold. First, it presents a novel local / part-based automatic system for forensic signature verification involving disguised signatures. Disguised signatures are written by authentic authors but with the intention of later denial. The proposed system reaches an equal error rate of 3.36% in classifying disguised and genuine signatures. Second, it compares...
Optical Character Recognition (OCR) is an important task with the rapid growth of the digital computers, online information services, PDAs and for conversion of text documents into digital text. This task enhances preservation of records and makes the access to documents easier. So, Baseline detection is an important step in the OCR because it directly affects the rest of the steps and increase the...
The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic casework. In collaboration with the German Research Center for Artificial Intelligence we have organized...
This competition scenario aims at a performance comparison of several automated systems for the task of signature verification. The systems have to rate the probability of authorship and non-authorship of signatures. In particular they have to determine whether questioned signatures are simulated disguised or the normal signature of the reference writer. Furthermore, the results will be compared to...
Face recognition has a great demands in human authentication and it becomes one of the most intensive field of biometrics research areas. In this paper, we present a bio-inspired face recognition system based on linear discriminant analysis and external clue i.e. geometrical features. The use of external clue helps to identify the face among very close match and secondly it also helps in the creation...
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