Teik-Toe Teoh

Intelligent face locator for smartphone

The market for smartphones is set for serious expansion. Juniper Research [3] is predicting the annual sales of smartphones will swell to 300 million by 2013 - up from about 153 million in year 2008, a rise of around 95 percent. Therefore, the development of mobile phone software well deserves wider attention. This paper introduces a real-time face processing application that was tested in Windows Mobile environment. This work is targeted towards the development of an efficient and intelligent face recognition system. The system is capable of locating the face region using derivative-based filtering, and classifying human face through the use of AdaBoost classifier. The motivation behind this work is that we aim to develop a robust model that can help to locate face for a portable face recognition application. The experiments carried out show that we have achieved the features of mobile application: speed and efficiency, that is able to deploy facial recognition into smartphone.

Towards a portable intelligent facial expression recognizer

Facial expression recognition is a challenging task. A facial expression is fonned by contracting or relaxing different facial muscles on human face which results in temporally deformed facial features like wide open mouth, raising eyebrows or etc. Such a system presents challenges. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometime conventional classifiers are not even effective to handle those features and then produce good classification perfonnance. This paper discusses the issues on how the advanced feature selection techniques together with good classifiers can playa vital important role of real-time facial expression recognition. The content of this paper is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

Notice of Retraction Human emotional states modeling by Hidden Markov Model

This paper presents an attempt of using Hidden Markov Model to model the high level emotions (such as, encouraging, interest, unsure, disagreeing and discouraging) through low level facial expressions (such as, happy, sad, surprise and neutral). The rationale behind using HMM is that the HMM models human brain as human emotion is quite complex, naturally a human instinct contain hidden layer as well (like sub conscious mind). In addition, Markov state chain property is good to model human emotion as our emotion is also through our mind state that it is always dependent on our previous state of our emotion and current event will end up our current emotion state. Our proposed work is to develop an emotion indexer acting as a higher level analysis to interpret more advanced emotional states out of the basic emotions.

Development of an Intelligent Facial Expression Recognizer for Mobile Applications

In the light of fast pace smart phone development, increasing numbers of applications are being developed to cater for portability. A real-time facial expression recognition application is develop that was tested in Windows Mobile environment. The underlying algorithm adopted in this work uses Boosting Naive Bayesian (BNB) approach for recognition. We examine the structure of training data and the effect of attributes on the class probabilities through the use of Naive Bayesian classifier (NBC). The experiments carried out show that we have achieved the important features of mobile application: speed and efficiency. This work is believed to be the first recorded initiative that de-ploys facial expression recognition into a mobile phone. It seeks to provide a launching point for a sound and portable mobile application that is capable of recognizing different facial expressions.

Emotional prediction using time series multiple-regression genetic algorithm for autistic syndrome disorder

Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades. Recent research activities in time series forecasting indicate that two basic limitations detract from their popularity for financial time series forecasting: (a) ARIMA models assume that future values of a time series have a linear relationship with current and past values as well as with white noise, so approximations by ARIMA models may not be adequate for complex nonlinear problems; and (b) ARIMA models require a large amount of historical data in order to produce accurate results. Both theoretical and empirical findings have suggested that integration of different models can be an effective method of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, ARIMA models are integrated with Artificial Neural Networks (ANNs) and Fuzzy logic in order to overcome the linear and data limitations of ARIMA models, thus obtaining more accurate results. Empirical results of forecasting model indicate that the hybrid models exhibit effectively improved forecasting accuracy so that the model proposed can be used as an alternative to financial market forecasting tools. In this paper, experiments were conducted to confirm these hypotheses by evaluating the predictive capability of the developed ensemble of models in the domain of emotion prediction. This work attempts to anticipate subsequent emotion given historical emotions recorded.

Shopping experience and their influence on satisfaction in Australia and Indonesia

This study investigates the influence of shopping experience in terms of store atmosphere and cognitive responses on store patronage satisfaction. The paper reports the results of a cross-cultural survey carried out in Perth (Australia) and Surabaya (Indonesia). There are four hypotheses tested in the study. The first hypothesis is the shopper’s cogntive responses are associated with store atmosphere; and the second hypothesis is store patronage satisfaction is associated with the cognitive responses of customers.