This paper describes the use of statistical techniques and Hidden Markov Models (HMM) in the recognition of emotions. The method aims to classify 6 basic emotions (angry, dislike, fear, happy, sad and surprise [4]) from both facial expressions (video) and emotional speech (audio). The emotions of 2 human subjects were recorded and analyzed. The findings show that the audio and video information can be combined using a rule-based system to improve the recognition rate.
Increasing public awareness of food quality and safety has prompted a rapid increase in food authentication of halal food, which covers the production method, technical processing, identification of undeclared components, and species substitution in halal food products. This urges for extensive research into analytical methods to obtain accurate and reliable results for monitoring and controlling the authenticity of halal food. Nonetheless, authentication of halal food is often challenging because of the complex nature of food and the increasing number of food adulterants that cause detection difficulties. This review provides a comprehensive and impartial overview of recent studies on the analytical techniques used in the analysis of halal food authenticity (from 1980 to the present, but there has been no significant trend in the choice of techniques for authentication of halal food during this period). Additionally, this review highlights the classification of different methodologies based on validity measures that provide valuable information for future developments in advanced technology. In addition, methodological developments, and novel emerging techniques as well as their implementations have been explored in the evaluation of halal food authentication. This includes food categories that require halal authentication, illustrating the advantages and disadvantages as well as shortcomings during the use of all approaches in the halal food industry.
In this paper we describe a system that automatically detects and recognizes human head gestures such as nodding and shaking in complex background conditions using a cheap Web Cam under uncontrolled conditions.The images of the head, captured a t 20 frames per second, are very noisy and are of a low resolution. In the proposed system, the invariant moments of each image captured is extracted and is fed into a recognition system that uses discrete Hidden Markov Models (HMMs) to classify the head gestures. The system achieves an average success rate of 87%. The system can successfully run on any low to high end PC connected to a USB Web Cam without any manual initialization.
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