Hazry Desa

Extraction of head and hand gesture features for recognition of sign language

Sign language is the primary communication method that impaired hearing people used in their daily life. Sign language recognition has gained a lot of attention recently by researchers in computer vision. Sign language recognition systems in general require the knowledge of the hands position, shape, motion, orientation and facial expression. In this paper we present a simple method for converting sign language into voice signals using features obtained from head and hand gestures which can be used by hearing impaired person to communicate with an ordinary person. A simple feature extraction method based on the area of the objects in a binary image and Discrete Cosine Transform (DCT) is proposed for extracting the features from the video sign language. A simple neural network models is developed for the recognition of gestures using the features computed from the video stream. An audio system is installed to play the particular word corresponding to the gestures. Experimental results demonstrate that the recognition rate of the proposed neural network models is about 91%.

A differential steering control with proportional controller for an autonomous mobile robot

In this paper, differential steering control with proportional controller method are developed. In the steering control of mobile robot, the underlying dynamics of processes are often highly complex due to operating problems such as actuator constrains, time delay and disturbances. Because of these reasons, many control system of mobile robots require extensive retuning the control parameter and the worst cases may result in redesigning or change the control program and hardware. To solve the above mentioned problems, we use proportional control method. Based on the model proportional control method, we predict the path that the mobile robot will follow by using the current velocities of the right wheel and the left wheel which update the real-time current position of mobile robot. The model proportional method is to overcome time delay cause by slow response of the sensor and other dynamic processes. The outputs from the control are the velocity and angular velocity of mobile robot. From these velocity and angular velocity of mobile robot we determine the number of encoder pulses for the right wheel and left wheel. The number of encoder pulses for the right wheel and left wheel are input to the right DC motor and to the left DC motor of mobile robot to generate the velocity of each wheel. The proportional controller is used to produce the same speed of the right wheel and the left wheel in order to make the mobile robot move in a straight line. It also used to produce the desired speed of the right wheel and left wheel for steering control to make left or right turning.

Particle Swarm Optimization algorithm for facial emotion detection

Particle Swarm Optimization (PSO) algorithm has been applied and found to be efficient in many searching and optimization related applications. In this paper, we present a modified version of the algorithm that we successfully applied to facial emotion detection. Our approach is based on tracking the movements of facial action units (AUs) placed on the face of a subject and captured in video clips. We defined particles that form swarms such that they have a component around the neighborhood of each AU. Particles are allowed to move around the effectively n-dimensional search space in search of the emotion being expressed in each frame of a video clip (where n is the number of action units being tracked). We have implemented and tested the algorithm on video clips that contain three of the six basic emotions, namely happy, sad and surprise. Our results show the algorithm to have a promising success rate.

Gesture recognition system for Kod Tangan Bahasa Melayu (KTBM) using neural network

This paper presents simple methods for translating Kod Tangan Bahasa Melayu (KTBM) into voice signal based on subject head and two hand gestures. Different gesture signs made by different subjects are captured using a USB web camera in RGB video stream format with a screen bit depth of 24 bits and a resolution of 320 × 240 pixels. The recorded video of the sign language is divided into number of image frames. Using a simple segmentation technique, the frame image is segmented into three region namely, head region, left hand region and right hand region. After performing the image segmentation, the image frames are converted into binary image format. A simple feature extraction method is then applied and the variations of the features in the subsequent frame are modeled using Discrete Cosine Transform (DCT). The features extracted are associated to the equivalent voice sound and a simple neural network model trained by error prob method is developed. An audio system is used to play the equivalent voice signal from the recognized sign language. Experimental results demonstrate that the recognition rate of the proposed neural network models is about 81.07%.

Facial emotion detection using GPSO and Lucas-Kanade algorithms

Emotion detection is receiving a lot of attention from researchers due to its potentials in improving humancomputer interaction. Recently, we proposed a modification to the Particle Swarm Optimization (PSO) algorithm for the purpose applying it to emotion detection. Our algorithm, which we called Guided Particle Swarm Optimization (GPSO), involves studying the movements of specific points, called action units (AUs), placed on the face of a subject, as the subject expresses different emotions. A swarm of particles is defined such that each particle consists of components from the neighborhood of each AU. However, instead of applying the pure PSO on the swarm to detect emotions, we made the algorithm to take into account the positions of the AUs – thus, the swarm is effectively guided to converge on the path of the AUs. We showed this approach to work very well and made the swarm to converge very quickly to identify the emotion being expressed. One limitation to our earlier system was that the AUs must be physically specified on the subject before the video clips are recorded. In this paper, we present an improvement on the system where we specify the AUs at runtime in a video stream and then apply LK algorithm to keep track of their positions, thus making the system to work on real time basis with the same promising detection success rates. Potential application areas of our system include medical engineering, forensic applications by police and psychiatric applications.

Real-time system for facial emotion detection using GPSO algorithm

Particle Swarm Optimization (PSO) algorithm has been widely recognized as an efficient algorithm with applicability is many areas. Recently, we proposed the Guided Particle Swarm Optimization (GPSO) algorithm, which is a modification to PSO designed for facial emotion detection. GPSO involves keeping track of relevant points, called Action Units (AUs), which are specified on the face of a subject. Swarm of particles was defined such that each particle has a component within the neighborhood of each AU. We implement GPSO into a facial emotion detection system that can detect the six basic universal emotions. The system was tested on a variety of subjects and the results realized were very promising. However, application of the system was limited to pre-recorded video clips because the AUs must be pre-processed to obtain their positions over time, which were then fed as input to the system. In this paper, we present an improvement we made to the system by applying Lucas-Kanade (LK) optical flow algorithm. LK algorithm allowed us to keep track of the positions of the AUs in real-time, thereby eliminating the need for preprocessing. The improved system is now a real-time system that works on video streams to identify facial emotions and achieved the same promising detection success rates as the original system.

The Development of a Web-Based Claims System

The main purpose of this study is to develop a computer system for University Malaysia Perlis (UniMAP) staff to make claims via electronic media. Since this system is Web-based, staff can make claims anywhere, anytime and at any locations. This method can overcome not only human errors but also more efficient, fast and accurate. Therefore, this system can also save time, effort, and administrative costs. In this study, Active Server Pages (ASP) has been chosen to make the calculation and also to generate reports. After the system has been developed, a test was conducted using forms that have been simulated manually. The purpose is to enable the researcher to make comparison with the ones made using the developed system in order to detect errors or flaws from the manual simulation in the system.

Remote access of SCADA with online video streaming

Owners of industries traveling around the world need some system from where they can monitor and keep control of their industry remotely. Speedily advancing software technologies have made it possible to develop a new generation of Supervisory Control and Data-Acquisition (SCADA) system. This generation of SCADA is known as PROPOSED SCADA SYSTEMS. This paper present a concept of how industrial and commercial areas can be monitored and controlled via SCADA system from anywhere in the world on supported portable devices. Through Remote access of SCADA, the Remote Desktop Protocol (ROP) brings SCADA real-time and passes information to users. The presented solution provides reduced software costs while improving reliability. The system also allows Open Process Control (OPC) linkage between the controller and SCADA systems. An additional monitoring approach in this system is lP CAMERAS based online visual monitoring. Life videos of the industry and commercial area can be viewed from anywhere in the globe on the portable devices. The proposed concept is simulated on two prototype industrial plants 1) An automated Car Wash System and 2) Load Cell based luggage segregation. Proposed system was tested on simulator and show remarkable results.

Fuzzy clustering for genetic algorithm based optimized ellipse data in classifying face emotion

In this paper, lip and eye features are applied to classify the human emotion using a set of irregular and regular ellipse fitting equations using genetic algorithm (GA). A South East Asian face is considered in this study. The parameters relating the face emotions, in either case, are entirely different. All six universally accepted emotions and one neutral are considered for classifications. The method which is fastest in extracting lip and eye features is adopted in this study. Observation of various emotions of the subject lead to unique characteristic of lips and eyes. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. One ellipse based fitness function is proposed for the eye configuration. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters.

Investigation of Steering Wheel Control of an Electric Buggy Car for Designing Fuzzy Controller

Steering control for path tracking and navigation are important for the autonomous vehicle. A good steering control system can determine the success of autonomous navigation through designed paths. Comfort and safety for the passenger are the main concerns in developing a controller for an autonomous electric vehicle (AEV). Comfort and the safe autonomous system can be achieving by imitating human intelligence and decision-making ability into the controller. A GPS module couple with a fuzzy controller to follow the designed path. Steering is control by using brushless DC motor with certain gear configuration. In order to achieve better drive performance for the autonomous vehicle, the behaviors of human subjects are studied and investigated. Investigation of steering angle on 3 different paths is designed to study the driving patterns by the human subjects, which are straight, turn right and turn left. The results show satisfactory outcomes as the subject navigates through the designed path with the similar patterns. The average value of steering wheel angle for the straight, right and left path are 13°, −151°, and 237°, respectively. The maximum angle to turning to the left and right are 286° (subject #1) and −226° (subject #1). This paper consists of the construction of a Fuzzy logic controller to control steering wheel and experiments set-up to develop the Fuzzy controller for an autonomous vehicle.