Siti Zainab Ibrahim

Face recognition with Symmetric Local Graph Structure (SLGS)

Face recognition demonstrates the significant progress in the research field of biometric and computer vision. The fact is due to the current systems perform well under relatively control environments but tend to suffer when the present of variation in pose, illumination, and facial expression. In this work, a novel approach for face recognition called Symmetric Local Graph Structure (SLGS) is presented based on the Local Graph Structure (LGS). Each pixel is represented with a graph structure of its neighbours’ pixels. The histograms of the SLGS were used for recognition by using the nearest neighbour classifiers that include Euclidean distance, correlation coefficient and chi-square distance measures. AT&T and Yale face databases were used to be experimented with the proposed method. Extensive experiments on the face database clearly showed the superiority of the proposed approach over Local Binary Pattern (LBP) and LGS. The proposed SLGS is robust to variation in term of facial expressions, facial details, and illumination. Due to good performance of SLGS, it is expected that SLGS has a potential for application implementation in computer vision.

A Framework on Cloud Based Connected Car Services

The connected car is a loaded term describing all the technological advances happening inside automobiles with assistance of cloud technology to transfer information. The best known connected-car technology is satellite navigation, which uses the global-positioning system (GPS) simultaneously with a database of roads to provide directions and find points of interest. Following globalized technology era, many consumers are now adding internet connectivity to their cars in portable device which acts as the “smart” phone. Besides that, a two-way internet link allows for more detailed forms of navigation, and also makes it possible to gather and accumulate information from small to large numbers of vehicles. Smartphone’s is continuously changing how consumers interact with the world around them. Connected car with cloud technology can broaden this interactive dynamic to drivers on the road. In this paper, we proposed to deliver in-car experience for drivers or passengers using cloud technology.

Real Time Driver Anger Detection

The field of artificial intelligence has seen an increasing number of researches being done related to facial expression recognition. Different methods have been proposed with some of them yielding good results and some performing poorly. Apart from that, anger plays a pivotal role in road accidents since road rage is stated to be one of the contributing factors to road accidents. In order to cater road rage and considering it being harmful to drivers and passengers, this paper proposes a real time driver anger detection. The project classifies human facial expressions, mainly anger expression in real time from a live video in order to warn the driver and eventually road accidents can be reduced.

A Study of Wireless Communication Technologies for Vehicular Communication

In recent years, vehicle communication is an advanced technology that has attain attention in both industries and academician all over the world. The initiation on vehicular communication is to improve road safety, efficiency and comfort. This paper studies the availability of the wireless communication technologies for vehicular communication and the possible implementation of the suitable wireless communication for vehicle communication in the context of Malaysia.

Driver Fatigue Detection

Driver fatigue detection is a term used to develop a nonintrusive system which can detect fatigue of the driver and issue a timely warning. The idea behind this project is to monitor the driver’s eyes using a camera in real time and to develop algorithm that can detect driver fatigue. Besides, the system also produces warning output in a form of sound once fatigue is detected.

Physiological measurement used in real time experiment to detect driver cognitive distraction

This paper discusses about lips and eyebrows are used to detect driver cognitive distraction by using faceAPI toolkit. A few number of classification algorithms like Support Vector Machine (SVM), Logistic Regression (LR) and Static Bayesian Network (SBN) and Dynamic Bayesian Network (DBN) have been used for accuracy rate comparison.