Kalaiarasi Sonai Muthu

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.

Multi-instance finger vein recognition using minutiae matching

Among the various multi-modal biometric approaches, multi-instance biometric appears to be understudied despite it inherits the merits of multimodal biometrics system. Multi-instance biometrics is useful when the signal quality is too low for robust verification. As compared to other multi-modal approach, multi-instance fusion reduces the need of multiple acquisitions using different sensors and thus lessen both transaction time and sensor cost. In this work, we propose a reliable two-stage multi-instance finger vein recognition system based on minutiae matching method by integrating a unified minutia alignment and pruning approach using Genetic algorithm and the k-modified Hausdorff distance (k-MHD) measurement. The proposed method is evaluated by using the SDUMLA-HMT Finger Vein database. Experiments show the proposed method is able to attain promising recognition rate compared to its single biometrics counterpart. The best result is achieved by applying the k-nearest neighbor measurement alongside, where the recognition rate can be up to 99.7% when MHD is used for matching.

Genetic-optimized classifier ensemble for cortisol salivary measurement mapping to electrocardiogram features for stress evaluation

This work presents our findings to map salivary cortisol measurements to electrocardiogram (ECG) features to create a physiological stress identification system. An experiment modelled on the Trier Social Stress Test (TSST) was used to simulate stress and control conditions, whereby salivary measurements and ECG measurements were obtained from student volunteers. The salivary measurements of stress biomarkers were used as objective stress measures to assign a three-class labelling (Low-Medium-High stress) to the extracted ECG features. The labelled features were then used for training and classification using a genetic-ordered ARTMAP with probabilistic voting for analysis on the efficacy of the ECG features used for physiological stress recognition. The ECG features include time-domain features of the heart rate variability and the ECG signal, and frequency-domain analysis of specific frequency bands related to the autonomic nervous activity. The resulting classification method scored approximately 60-69% success rate for predicting the three stress classes.

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.