Neurometrics, Machine Learning & Genetic Algorithms: A Literature Review

Exploring the Impact of Genetic Algorithms and Neurometrics on Facial Expression Recognition and Advertising Strategies

Abstract

Facial expression is a natural nonverbal communication language. A person can express his or her sentiments/ state of mind through facial expressions but sometimes these expressions are not good enough for recognition systems, they have to be more refined to get right results. Consequently, developing a robust facial expression recognition system which can recognize facial expression in humans and can serve as an important component of natural human-machine interfaces is highly required. Support Vector Machine (SVM) among other algorithms has a very good generalization capability and dynamic classification scheme which makes it suitable for facial expression recognition. Support vector machines have previously been successfully employed in a variety of classification applications including identity and text recognition. This paper provides a brief review of researches done in the field of Genetic algorithm, Neurometrics, and Machine learning. After reviewing the existing literature the researcher feels that Genetic algorithm, Neurometrics, and Machine learning can be clubbed together to provide a framework which can help in determining the impacts of different advertisement strategies to find out the best suitable mode of advertisement for business practices.