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Committee Neural Networks for Image Based Facial Expression Classification System: Parameter Optimization

Lakumarapu, Shravan Kumar
http://rave.ohiolink.edu/etdc/view?acc_num=akron1277322041

Abstract Details

2010, Master of Science in Engineering, University of Akron, Biomedical Engineering.
There has been a significant growth in the application of Artificial Neural Networks (ANNs) in the medical field including clinical decision support systems which call for high reliability and performance often involving multi-classification problems. Committee Neural Networks (CNNs) were developed for increased reliability and performance and were successfully applied to several multi-classification problems. However, the effect of the number of input parameters on the performance of the CNNs has not been investigated. The purpose of the present study was to investigate this effect on the CNN performance in a multi-classification problem of facial image based mood detection. Kulkarni et al (2009) used 15 parameters to develop a CNN system for classifying a given facial image into one of the six basic moods. Six subsets of parameters, for each image, were extracted from the parameter database used by Kulkarni et al, with an increasing number of parameters (from five to ten). The entire data was divided into training data, initial testing data, and final evaluation data. The training data from the six subsets were used to train six groups of neural networks and these networks were subjected to initial testing using the corresponding initial testing data. CNNs of different sizes were formed for each group by selecting the best performing networks based on the initial testing results. These CNNs were further tested using the initial testing data and the best performing CNN from each group was selected for further evaluation. All the selected committees were further evaluated using the final testing data from subjects not used in training or initial testing. Two approaches were used for converting neural network output into binary: “winner takes all” approach and the “thresholding” approach. The results from both the approaches show that with an increasing number of input parameters, the accuracy increased initially and then decreased. The committee decision was more accurate than individual member networks‟ decision. The highest accuracies obtained were from the CNNs having eight input parameters: 87.9% using the “winner takes all approach” and 87.2% using the “thresholding” approach. In contrast, Kulkarni et al developed a dual committee system consisting of the primary committee and a specialized committee using 15 parameters and reported an accuracy of 90.4%. In the present study, a single committee using only eight input parameters produced similar accuracy. To increase the reliability of neural network based intelligent systems for medical applications, the number of parameters should be optimized.
Dr. Narender Reddy, Dr. (Advisor)
92 p.
Engineering
Artificial Neural Networks; Committee Neural Networks, Artificial Intelligence, decision support systems, optimization

Recommended Citations

Citations

  • Lakumarapu, S. K. (2010). Committee Neural Networks for Image Based Facial Expression Classification System: Parameter Optimization [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1277322041

    APA Style (7th edition)

  • Lakumarapu, Shravan Kumar. Committee Neural Networks for Image Based Facial Expression Classification System: Parameter Optimization. 2010. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1277322041.

    MLA Style (8th edition)

  • Lakumarapu, Shravan Kumar. "Committee Neural Networks for Image Based Facial Expression Classification System: Parameter Optimization." Master's thesis, University of Akron, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=akron1277322041

    Chicago Manual of Style (17th edition)

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This open access ETD is published by University of Akron and OhioLINK.