Aliaa A. A. Youssif

Automatic Facial Expression Recognition System Based on Geometric and Appearance Features

This paper presents a computer vision system for automatic facial expression recognition (AFER). The robust AFER system can be applied in many areas such as emotion science, clinical psychology and pain assessment it includes facial feature extraction and pattern recognition phases that discriminates among different facial expressions. In feature extraction phase a combination between holistic and analytic approaches is presented to extract 83 facial expression features. Expression recognition is performed by using radial basis function based artificial neural network to recognize the six basic emotions (anger, fear, disgust, joy, surprise, sadness). The experimental results show that 96% recognition rate can be achieved when applying the proposed system on person-dependent database and 93.5% when applying on person-independent one.

Fingerprint Recognition System Using Hybrid Matching Techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

Energy Aware and Adaptive Cross-Layer Scheme for Video Transmission Over Wireless Sensor Networks

Wireless multimedia sensor networks (WMSNs), is an ad hoc network of wirelessly connected sensor nodes that allow retrieving video and audio streams, still images, and scalar sensor data but such sensors are limited in energy, memory, communication, and computational power. Multimedia transmission over wireless sensor network (WSN) is a challenging task due to quality-of-service(QoS) guarantees such as huge amount of bandwidth, strict delay, and lower loss ratio. Recently, cross-layer approach adopted by WMSNs shows a promising approach that improves quality of multimedia transmitted over WSNs under different wireless conditions. In this paper, an energy aware and adaptive cross layer scheme to transmit multimedia content over WSNs is presented. It provides packet, queue, and path scheduling, so that it selects optimal video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type through an adaptive priority video queue so that less important packets are dropped in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority are routed through different paths with different QoSs considering network lifetime. Simulation results show that new scheme transmits video over WSNs efficiently and meets QoS requirements and uses energy wisely to prolongs network lifetime.

Arabic Sign Language (ArSL) Recognition System Using HMM

Hand gestures enabling deaf people to communication during their daily lives rather than by speaking. A sign language is a language which, instead of using sound, uses visually transmitted gesture signs which simultaneously combine hand shapes, orientation and movement of the hands, arms, lip-patterns, body movements and facial expressions to express the speakers thoughts. Recognizing and documenting Arabic sign language has only been paid attention to recently. There have been few attempts to develop recognition systems to allow deaf people to interact with the rest of society. This paper introduces an automatic Arabic sign language (ArSL) recognition system based on the Hidden Markov Models (HMMs). A large set of samples has been used to recognize 20 isolated words from the Standard Arabic sign language. The proposed system is signer-independent. Experiments are conducted using real ArSL videos taken for deaf people in different clothes and with different skin colors. Our system achieves an overall recognition rate reaching up to 82.22%.

Spontaneous Facial Expression Recognition Based on Histogram of Oriented Gradients Descriptor

Automatically detecting facial expressions has become an important research area. It plays a significant role in security, human-computer interaction and health-care. Yet, earlier work focuses on posed facial expression. In this paper, we propose a spontaneous facial expression recognition method based on effective feature extraction and facial expression recognition for Micro Expression analysis. In feature extraction we used histogram of oriented gradients (HOG) descriptor to extract facial expression features. Expression recognition is performed by using a Support vector machine (SVM) classifier to recognize six emotions (happiness, anger, disgust, fear, sadness and surprise). Experiments show promising results of the proposed method with recognition accuracy of 95% on static images while 80% on videos.

ACWSN: an adaptive cross layer framework for video transmission over wireless sensor networks

Abstract Wireless multimedia sensor networks (WMSNs), have limited resources in terms of computational, memory, bandwidth, and battery capability, which makes transmitting multimedia content over it, is a challenge as multimedia requires QoS guarantee. Recently adopting cross-layer design in WMSNs proved to be a promising approach, which improves quality of service of WSN under various operational conditions. In this work, an adaptive cross layer framework for transmitting multimedia content over WSN (ACWSN) is presented, it is based on an extensive study of Group Of Picture structure effect on quality of multimedia transmission in various wireless channel states that is executed at sink node. It adaptably selects optimum video encoding parameters at application layer according to current wireless channel state which is communicated from physical layer and recommendations communicated from sink node; in addition an AVQ (adaptive video queue) which schedule packets according to its type to drop less important packets in case of network congestion. Simulation results show that ACWSN optimizes video quality in different wireless channel conditions.

E-Assessment Tool: A Course Assessment Tool Integrated into Knowledge Assessment

An Electronic Assessment System (EAT) is introduced to improve the teaching process in an E-learning environment. The system provides the instructor with assistance to modify the course contents. Since student is a key element in teaching, EAT takes into account the student answer, answer-time and student feedback as parameters.

Self Adjusted Security Architecture for Mobile Ad Hoc Networks (MANETs)

In this work we present a novel security architecture for MANETs that merges the clustering and the threshold key management techniques. The proposed distributed authentication architecture reacts with the frequently changing topology of the network and enhances the process of assigning the nodes public key. In the proposed architecture, the overall network is divided into clusters where the clusterheads (CH) are connected by virtual networks and share the private key of the central authority (CA) using Lagrange interpolation. Experimental results show that the proposed architecture reaches to almost 95.5% of all nodes within an ad-hoc network that are able to communicate securely, 9 times faster than other architectures, to attain the same results. Moreover, the solution is fully decentralized to operate in a large-scale mobile network.

Computer Aided Recognition of Vocal Folds Disorders by Means of RASTA-PLP

In the context of the recognition of vocal folds disorders, the systems based on acoustic analysis are being introduced as computer aided medical diagnosis tools due to its objectivity and noninvasive nature. Acoustic analysis is a complementary tool to those methods based on direct observation of the vocal folds by laryngoscopy; also, it can be used for the evaluation of surgical operation. This paper presents a novel approach in voice pathology assessment using RASTA-PLP feature extraction method in the framework of a HMM. The proposed method then compared to other feature extraction methods such as MFCC and PLP. The experimental results show that RASTA-PLP attained 92.86% correct classification rates and AUC of 0.94 compared to 0.81 and 0.79 for MFCC and PLP respectively.

Natural Genetic Engineering of Hepatitis C Virus NS5A for Immune System Counterattack

The Hepatitis C virus nonstructural 5A (NS5A) protein is a hydrophilic phosphoprotein with diverse functions. The domain assignment of NS5A had been refined using a systematic in silico bioinformatics approach using DOMAC, the protein is divided into three domains and domain III is subdivided into two subdomains using ProDom and SSEP servers. The fold structure for domains II and III were predicted using the meta‐server 3D‐Jury. Scanning motif databases (SMART, BLOCKS, and PROSITE) gave new motifs. Two important motifs, the interleukins 1 and 8 interaction motifs, relating to NS5A function in inducing the interleukin 8 promoter, were discovered from the BLOCKS scan. Protein–protein interaction motifs were predicted as hot loops and disordered regions, corresponding to binding regions with the ds‐protein kinase R, viral polymerase, and Src homology 3 signaling proteins binding motif. Other hot loops were predicted in the V3 region and in the single‐stranded DNA‐binding protein motif. The different mechanisms by which the NS5A protein leads to immune system signaling dysfunction points to the natural genetic engineering of this protein.