Afizan Azman

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.

Non intrusive physiological measurement for driver cognitive distraction detection: Eye and mouth movements

Driver distractions can be categorized into 3 major parts:-visual, cognitive and manual. Visual and manual distraction on a driver can be physically detected. However, assessing cognitive distraction is difficult since it is more of an “internal” distraction rather than any easily measured “external” distraction. There are several methods available that can be used to detect cognitive driver distraction. Physiological measurements, performance measures (primary and secondary tasks) and rating scales are some of the well-known measures to detect cognitive distraction. This study focused on physiological measurements, specifically on a drivers eye and mouth movements. Six different participants were involved in our experiment. The duration of the experiment was 8 minutes and 49 seconds for each participant. Eye and mouth movements were obtained using the FaceLab Seeing Machine cameras and their magnitude of the r-values were found more than 60% thus proving that they are strongly correlated to each other.

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.

An empirical study to examine sex differences in cognitive distraction among drivers

There are basically 3 different types of driver distraction: manual, visual and cognitive. This paper focused on cognitive distraction on drivers. Cognitive distraction is occurred when a drivers mind is off from the road. Drivers are might probably see and realize objects and the environment on the road, and manually handle their vehicle safely, but their minds are thinking something that are not related to a driving safety issue. Cognitive distraction is quite difficult to be detected compare to manual and visual distraction, thus, it is the most dangerous type of distraction. This paper is to compare men and women distractions cognitively. A physiological measurement specifically on eyes has been used as the feature to detect drivers cognitive distraction. Information on blinking frequency, blinking duration, gaze rotation and pupil diameter has been captured using faceLab Seeing Machine cameras. Data were analyzed using independent sample t-test and means from each men and women have been presented in bar graphs.

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.

Real Time Customer Satisfaction Index

A Real Time and Automatic Customer Satisfaction Index is an application-based facial expression recognition system which is used to capture a person facial expression. The main idea of this system is to capture customer’s facial expressions while using a product or services and act as an assessment tool to evaluate customer satisfaction with subjective evaluation (questionnaire) to understand customer’s facial expressions. Hence, the system will enable product or services seller to get customer feedback immediately and it can help product or services seller to save time because the ordinary way to get customer feedback about a product or services is by doing a survey or interview.

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.

Eye and mouth movements extraction for driver cognitive distraction detection

Cognitive distraction is happened when a drivers mind is off the road. It happened when a driver is looking on the road but his mind is doing a thinking process. It has been found that, cognitive distraction is the most dangerous type of driver distractions. This has been presented in the comparison table and stem plot between Control Experiment result and Task Experiment result. Information from eye movement and mouth movement are obtained using the faceLab cameras and their correlation is discussed here. Two sets of experiment (Control and Task) with 6 participants were completed for this paper. Results were presented in scatter diagram to show the correlation between eye and mouth movements. Stem plot is to show the different result obtained between control and task experiment.