The past two decades has seen a dramatic increase in the amount of information or data being stor... more The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
Facial Expression Recognition is very broad domain in the field of Human Computer Interaction. Af... more Facial Expression Recognition is very broad domain in the field of Human Computer Interaction. Afterward to improve the recognition rate of within less time it becomes a broader research area. So in this discussion we mainly focus on different Machine Learning and Pattern Recognition techniques to improve in this field. In 21st century different researcher mainly focus in this field. Biometric recognition in the field of computer science has become very intensive interested area now days. Facial Expression Recognition has become very progressive domain because face is easy to interact to be intimate with human. It involves different steps to recognize expression. We discuss these steps below in detail. But it is requisite to detect face, extract feature from detected face then select best feature to recognize and then classify this face. Seven common facial expression include in this area of research. Different data-set and database are freely available like as JAFFEE, Yale and Red Boud etc. Here different techniques involves for detect face, feature extract, select best feature with classification are as PCA(Principal Component Analysis), LBP(Local Binary Pattern), HOG(Histogram Oriented Gradient), SVM(Support Vector Machine), ANN(Artificial Neu-ral Network) and many more are available. All these techniques used to recognize different human expressions in different condition and on various database as mentioned above. We delimit (define) general measures (steps) to recognize human Face Expression. Additionally an effort has been made to represent different techniques in tabular form to reach easily for quick reference.
The past two decades has seen a dramatic increase in the amount of information or data being stor... more The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
Facial Expression Recognition is very broad domain in the field of Human Computer Interaction. Af... more Facial Expression Recognition is very broad domain in the field of Human Computer Interaction. Afterward to improve the recognition rate of within less time it becomes a broader research area. So in this discussion we mainly focus on different Machine Learning and Pattern Recognition techniques to improve in this field. In 21st century different researcher mainly focus in this field. Biometric recognition in the field of computer science has become very intensive interested area now days. Facial Expression Recognition has become very progressive domain because face is easy to interact to be intimate with human. It involves different steps to recognize expression. We discuss these steps below in detail. But it is requisite to detect face, extract feature from detected face then select best feature to recognize and then classify this face. Seven common facial expression include in this area of research. Different data-set and database are freely available like as JAFFEE, Yale and Red Boud etc. Here different techniques involves for detect face, feature extract, select best feature with classification are as PCA(Principal Component Analysis), LBP(Local Binary Pattern), HOG(Histogram Oriented Gradient), SVM(Support Vector Machine), ANN(Artificial Neu-ral Network) and many more are available. All these techniques used to recognize different human expressions in different condition and on various database as mentioned above. We delimit (define) general measures (steps) to recognize human Face Expression. Additionally an effort has been made to represent different techniques in tabular form to reach easily for quick reference.
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