Principal Component Analysis and K-Nearest Neighbor Classifier for Facial Expression Recognition System

Manusia secara alami menggunakan ekspresi wajah untuk berkomunikasi dan menunjukan emosi mereka dalam berinteraksi sosial. Ekspresi wajah termasuk kedalam komunikasi non-verbal yang dapat menyampaikan keadaan emosi seseorang kepada orang yang telah mengamatinya. Penelitian ini menggunakan metode Principal Component Analysis (PCA) untuk proses ekstraksi ciri pada citra ekspresi dan metode Convolutional Neural Network (CNN) sebagai prosesi klasifikasi emosi, dengan menggunakan data Facial Expression Recognition-2013 (FER-2013) dilakukan proses training dan testing untuk menghasilkan nilai akurasi dan pengenalan emosi wajah. Hasil pengujian akhir mendapatkan nilai akurasi pada metode PCA sebesar 59,375% dan nilai akurasi pada pengujian metode CNN sebesar 59,386%.

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A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

International Journal of Electronics, Communications, and Measurement Engineering ◽

10.4018/ijecme.2019070101 ◽

2019 ◽

Vol 8 (2) ◽

pp. 1-25

Author(s):

Gopal Krishan Prajapat ◽

Rakesh Kumar

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Facial Expression ◽

Facial Expression Recognition ◽

Local Binary Pattern ◽

Recognition Rate ◽

Principal Component ◽

Component Analysis ◽

Expression Recognition ◽

Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

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A Modular Approach For Facial Expression Recognition using Euler Principal Component Analysis (e-PCA)

2018 IEEE Applied Signal Processing Conference (ASPCON) ◽

10.1109/aspcon.2018.8748418 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sujata ◽

Maitry Trivedi ◽

Suman K. Mitra

Keyword(s):

Principal Component Analysis ◽

Facial Expression ◽

Facial Expression Recognition ◽

Principal Component ◽

Component Analysis ◽

Modular Approach ◽

Expression Recognition

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Comparison of Region Based and Weighted Principal Component Analysis and Locally Salient ICA in Terms of Facial Expression Recognition

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011 - Studies in Computational Intelligence ◽

10.1007/978-3-642-22288-7_7 ◽

2011 ◽

pp. 81-89 ◽

Cited By ~ 1

Author(s):

Humayra Binte Ali ◽

David M. W. Powers ◽

Richard Leibbrandt ◽

Trent Lewis

Keyword(s):

Principal Component Analysis ◽

Facial Expression ◽

Facial Expression Recognition ◽

Principal Component ◽

Component Analysis ◽

Expression Recognition ◽

Weighted Principal Component

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An unsupervised learning approach for facial expression recognition using semi-definite programming and generalized principal component analysis

10.1117/12.839982 ◽

2010 ◽

Cited By ~ 3

Author(s):

Behnood Gholami ◽

Wassim M. Haddad ◽

Allen R. Tannenbaum

Keyword(s):

Principal Component Analysis ◽

Facial Expression ◽

Unsupervised Learning ◽

Facial Expression Recognition ◽

Principal Component ◽

Component Analysis ◽

Learning Approach ◽

Expression Recognition

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Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

Jurnal Teknologi dan Sistem Komputer ◽

10.14710/jtsiskom.6.4.2018.129-134 ◽

2018 ◽

Vol 6 (4) ◽

pp. 129-134 ◽

Cited By ~ 4

Author(s):

Jumoke Falilat Ajao ◽

David Olufemi Olawuyi ◽

Odetunji Ode Odejobi

Keyword(s):

Character Recognition ◽

Nearest Neighbor ◽

Recognition System ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbor ◽

Chain Code ◽

Nearest Neighbor Classifier ◽

Digitized Images ◽

Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

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