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A Proposed Framework for Emotion Recognition Using Canberra Distance Classifier

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Facial Expression Recognition has become the preliminary research area due to its importance in human-computer interaction. Facial Expressions conveys the major part of information so it has vast applications in various fields. Many techniques have been developed in the literature but there is still a need to make the current expression recognition methods efficient. This paper represents proposed framework for face detection and recognizing six universal facial expressions such as happy, anger, disgust, fear, surprise, sad along with neutral face. Viola-Jones method and Face Landmark Detection method are used for face detection. Histogram of oriented gradients is used for feature extraction due to its superiority over other methods. To reduce the dimensionality of features Principal Component Analysis is used so that the maximum variation is preserved. Canberra distance classifier is used for classifying the expressions into different emotions. The proposed method is applied on Japanese Female Facial Expression Database and have evaluated that the proposed method outperforms many state-of-the-art techniques.
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... It involves various features which are used in the observation, featuring and identification of the datasets. It is used in image retrieval [4] 5. Canberra distance (CanD) CanD(x,y)=∑ [5] Intrusion detection in computer systems and comparison of ranked lists [5] 6. The Sorensen distance SD(x,y)= ...
... It involves various features which are used in the observation, featuring and identification of the datasets. It is used in image retrieval [4] 5. Canberra distance (CanD) CanD(x,y)=∑ [5] Intrusion detection in computer systems and comparison of ranked lists [5] 6. The Sorensen distance SD(x,y)= ...
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... Canberra Distance [16] is a metric that measures the difference between two vectors or points in a multidimensional space. This metric is often used in data analysis, especially in cases where the data has high dimensions and contains various attributes. ...
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... In our previous work [16], we have used HOG features and used Canberra distance for classifying the facial expressions into emotions whereas in [17] we used fusion of LBP and HOG features with Canberra distance classifier. Table 1 summaries the comparison of all the previous papers. ...
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Face expression recognition is a typical task to make human and machine interaction possible. Besides this, medical science and other applications demand for such system. This paper focusses on importance of face detection and its feature parts. For this, Viola — Jones algorithm was implemented. The crucial part of this paper is feature extraction and the algorithm used for the purpose is modified local binary patterns algorithm. The results of feature extraction algorithms are compared to that in the literature to show the limitations of the existing algorithms and their suitabilility for this application. Neural network based approaches are used for classification of facial expressions. This paper enumerates two classification techniques for the facial expressions. The system performs well by the method used by us and gives efficient results. This was experimented using the Taiwanian database and the Japanese database.
Robust Facial Expression Classification Using Shape and Appearance Features
  • S L Happy
  • A Routray
Happy, S.L. and Routray, A., 2015. Robust Facial Expression Classification Using Shape and Appearance Features. 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), IEEE.
Face detection and recognition using Viola-Jones with PCA-LDA and square Euclidean distance
Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-Dabbagh, Wael Esam Matti and Mustafa Abdul Sahib Naser, 2016. Face detection and recognition using Viola-Jones with PCA-LDA and square Euclidean distance. International Journal of Advanced Computer Science and Applications (IJACSA), 7(5).