Proceedings ArticleDOI
Comparative analysis of machine learning KNN, SVM, and random forests algorithm for facial expression classification
Ratna Astuti Nugrahaeni,Kusprasapta Mutijarsa +1 more
- pp 163-168
TLDR
This paper compare and analyse the performance of three machine learning algorithm to do the task of classifying human facial expression, using the total of 23 variables calculated from the distance of facial features as the input for the classification process.Abstract:
Human depicts their emotions through facial expression or their way of speech. In order to make this process possible for a machine, a training mechanism is needed to give machine the ability to recognize human expression. This paper compare and analyse the performance of three machine learning algorithm to do the task of classifying human facial expression. The total of 23 variables calculated from the distance of facial features are used as the input for the classification process, with the output of seven categories, such as: angry, disgust, fear, happy, neutral, sad, and surprise. Some test cases were made to test the system, in which each test cases has different amount of data, ranging from 165–520 training data. The result for each algorithm is quite satisfying with the accuracy of 75.15% for K-Nearest Neighbor (KNN), 80% for Support Vector Machine (SVM), and 76.97% for Random Forests algorithm, tested using test case with the smallest amount of data. As for the result using the largest amount of data, the accuracy is 98.85% for KNN, 90% for SVM, and 98.85% for Random Forests algorithm. The training data for each test case was also classified using Discriminant Analysis with the result 97.7% accuracy.read more
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Minghan Cheng,Xiyun Jiao,Yadong Liu,Mingchao Shao,Xun Yu,Yi Bai,Zixu Wang,Siyu Wang,Nuremanguli Tuohuti,Shuaibing Liu,Lei Shi,Dameng Yin,Xiao Huang,Chenwei Nie,Xiuliang Jin +14 more
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Sparse coding-based representation of LBP difference for 3D/4D facial expression recognition
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References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
A Practical Guide to Support Vector Classication
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
Book
Discriminant Analysis and Statistical Pattern Recognition
TL;DR: In this article, the authors provide a systematic account of the subject area, concentrating on the most recent advances in the field and discuss theoretical and practical issues in statistical image analysis, including regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule.
Journal ArticleDOI
Facial expression recognition from video sequences: temporal and static modeling
TL;DR: This work introduces and test different Bayesian network classifiers for classifying expressions from video, focusing on changes in distribution assumptions, and feature dependency structures, and proposes a new architecture of hidden Markov models (HMMs) for automatically segmenting and recognizing human facial expression from video sequences.