Fingerprints are expanding in popularity, and the fingerprint datasets are becoming increasingly huge; they are recorded using a range of sensors embedded in smart devices like mobile phones and personal computers. The difficulty of fingerprint recognition systems is worsened when they are obtained using different sensors, which is one of the main challenges. Fingerprints can be categorized in a database to reduce the search space and speed up the query response. However, classifying cross-sensor fingerprints is a challenging problem. An efficient and robust solution is to use a convolutional neural network (CNN), but designing its architecture is time-consuming. In order to automatically design a CNN model for fingerprint classification, we developed a strategy that uses pyramidal clustering, principal component analysis (PCA), and the ratio of the between-class scatter to within-class scatter to determine the number of filters and the number of layers in the model automatically. It aids in the building of lightweight CNN models that are efficient and speed up fingerprint classification. We validated the proposed method on two benchmark datasets, FingerPass and FVC2004, which feature noisy, low-quality fingerprints obtained via live scan devices and various sensors. Compared to existing fingerprint classification methods and well-known pre-trained models, the newly developed models perform noticeably better.

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

Real-word (also known as semantic or context-sensitive) spelling error is a class of error that escapes the typical spell checker which relies on dictionary look-up. This kind of error occurs when a user types a correctly spelled word-by mistake-when another is intended, e.g., “I want a peace (piece) of cake.” Further, these errors commonly arise in text written by people with dyslexia. Real-word errors are harder to detect as we need to consider the context. In this paper, we propose a spell checker that detects and corrects real-word errors for the Arabic language. Our method avoids predefined confusion sets-a simple approach used by many works tackling this problem-which limits the list of words that can be detected and corrected. Thus, our system can detect and correct a larger set of real-word errors. For the detection phase, we employ word and stem n-gram (n = 1-3) language model along with machine learning, achieving a precision and recall of 83.5% and 99.2%, respectively. And for the correction phase we use n-gram, which results in an accuracy of 98%. Our scheme is robust, with an excellent performance even when the percentage of real-word error words is high. This makes the system suitable for handling errors in post OCR recognition of Arabic text.

Web access and web resources open many horizons, their usage increases in all life aspects including government, education, commerce and entertainment, where the key to such resources lies in Web browsers. Acknowledging the importance of universal accessibility to web resources, the W3C has developed a series of guidelines into a Web Accessibility Initiative (WAI), with the goal of providing access to web resources for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, the authors believe that the development of novel assistive technologies using new human-computer interfaces will go a long way towards achieving this lofty goal. In this paper, they present a P300 Electroencephalography Brain-controlled Web browser to enhance the accessibility of people with severe motor disabilities to Web resources. It enhances their interaction with the Web taking their needs into account. The proposed Web browser satisfies the Mankoff's requirements of a system that would “allow true web access.”

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.

Due to the availability of easy-to-use and powerful image editing tools, the authentication of digital images cannot be taken for granted and it gives rise to non-intrusive forgery detection problem because all imaging devices do not embed watermark. We investigated the detection of copy-move and splicing, the two harmful types of image forgery, using textural properties of images. Tampering distorts the texture micro-patterns in an image and texture descriptors can be employed to detect tampering. We did comparative study to examine the effect of two state-of-the-art best texture descriptors: Multiscale Local Binary Pattern (Multi-LBP) and Multiscale Weber Law Descriptor (Multi-WLD). Multiscale texture descriptors extracted from the chrominance components of an image are passed to Support Vector Machine (SVM) to identify it as authentic or forged. The performance comparison reveals that Multi-WLD performs better than Multi-LBP in detecting copy-move and splicing forgeries. Multi-WLD also outperforms state-of-the-art passive forgery detection techniques.

This paper proposes a method for race recognition from face images using local descriptors. The proposed method uses two types of local descriptors: local binary pattern (LBP) and Weber local descriptors (WLD). First, LBP and WLD histograms are obtained separately from blocks of normalized face image. Kruskal-Wallis feature selection technique is applied to the histograms to select the significant bins for race recognition. Then the selected bins from the two histograms are concatenated block by block to produce the final feature set of the face image. Minimum city block distance is used as a classifier. The experiments are conducted using gray scale FERET images with five race groups. Experimental results show that the proposed method has superior race recognition accuracies for all the five race groups compared to LBP and WLD alone.

Virtual Reality (VR) systems are used to distract patients from their current condition and immerse them in a virtual world where they are able to do many virtual activities while receiving their treatment. It was observed that VR systems improve the patent state of mind thereby improving their immune system. In this paper we study the role of VR in improving pain threshold for chemotherapy and burn patients. Unfortunately not all patients are able to use VR equipments, for example burn patents are unable to use VR head mounts and gloves. To design a VR software and hardware that could be used for every patent is close to impossible. In this paper we introduce the benefits and challenges of using VR systems as an added therapy to reduce pain sensation; we also give the general technical requirements for such system.