Visual Speech Recognition

Lip reading is used to understand or interpret speech without hearing it, a technique especially mastered by people with hearing difficulties. The ability to lip read enables a person with a hearing impairment to communicate with others and to engage in social activities, which otherwise would be difficult. Recent advances in the fields of computer vision, pattern recognition, and signal processing has led to a growing interest in automating this challenging task of lip reading. Indeed, automating the human ability to lip read, a process referred to as visual speech recognition (VSR) (or sometimes speech reading), could open the door for other novel related applications. VSR has received a great deal of attention in the last decade for its potential use in applications such as human-computer interaction (HCI), audio-visual speech recognition (AVSR), speaker recognition, talking heads, sign language recognition and video surveillance. Its main aim is to recognise spoken word(s) by using only the visual signal that is produced during speech. Hence, VSR deals with the visual domain of speech and involves image processing, artificial intelligence, object detection, pattern recognition, statistical modelling, etc.Comment: Speech and Language Technologies (Book), Prof. Ivo Ipsic (Ed.), ISBN: 978-953-307-322-4, InTech (2011

On Identifying Terrorists Using Their Victory Signs

In certain cases, the only evidence to identify terrorists, who are seen in digital images or videos is their hands’ shapes, particularly, the victory sign as performed by many of them when they intentionally hide their faces, and/or distort their voices. This paper proposes new methods to identify those persons for the first time from their victory sign. These methods are based on features extracted from the fingers areas using shape moments in addition to other features related to fingers contours. To evaluate the proposed methods and to show the feasibility of this study we have created a victory sign database for 400 volunteers using a mobile phone camera. The experimental results using different classifiers show encouraging identification results; as the best precision/recall were achieved by merging normalized features from both methods using linear discriminate analysis classifier with 96.6% precision and 96.3 recall. Such a high performance achieved by the proposed methods shows their great potential to be applied for terrorists’ identification from their victory sign

A Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images

Deep Convolutional Neural Networks (CNNs) are widespread, efficient tools of visual recognition. In this paper, we present a comparative study of three popular pre-trained CNN models: AlexNet, VGG-16 and VGG-19. We address the problem of palmprint identification in low-quality imagery and apply Support Vector Machines (SVMs) with all of the compared models. For the comparison, we use the MOHI palmprint image database whose images are characterized by low contrast, shadows, and varying illumination, scale, translation and rotation. Another, high-quality database called COEP is also considered to study the recognition gap between high-quality and low-quality imagery. Our experiments show that the deeper pre-trained CNN models, e.g., VGG-16 and VGG-19, tend to extract highly distinguishable features that recognize low-quality palmprints more efficiently than the less deep networks such as AlexNet. Furthermore, our experiments on the two databases using various models demonstrate that the features extracted from lower-level fully connected layers provide higher recognition rates than higher-layer features. Our results indicate that different pre-trained models can be efficiently used in touchless identification systems with low-quality palmprint images.Comment: 5 pages, 5 figures, Ninth Hungarian Conference on Computer Graphics and Geometry, Budapest, 201

Developing a Geoinformatic-engineering Stability Modeling Method, Using Field Data and GIS Environment: a Case Study From Al Qarara Area in Wadi Musa, Jordan

DOI:10.17014/ijog.2.1.1-21By applying detailed geological field surveys, the spatial factors affecting geo-engineering stability were used to develop a geo-engineering stability modeling method to identify areas under potential threat of landsliding. The factors affecting geo-engineering stability in Al Qarara area in Petra-Jordan were studied and given assumed rates of importance, where optimization process was run by lag iterations; the produced spatial layers of the different factors were gathered and modeled using GIS; a final stability map was produced using an optimized equation. The produced map was validated qualitatively and quantitatively, where a comparison was made between the reality in the field and several maps of different equation. The modeling method which was developed in the context of this study proved to be suitable to produce micro-zonation maps of areas having landslide risk. Further applications on the method in other areas suffering landslides will further improve it