An initial study of feature extraction’s methods in facial expression recognition

Facial Expression Recognition is a sub-branch of Affective Computing that specializes in extracting human emotions and facial features from visual input (images and videos) and tagging them to specific emotion hierarchies. The difficulty of this task lies not only in the subjectivity of distinguishing between human emotional states but, also in the diversity of the human race and culture that influences how we humans perceive sentiments. This article aims to tackle this challenge through two distinct methods of image processing, meaning automatic, Artificial Intelligence (AI) driven feature extraction and manual feature extraction through classical approaches and compare the performance of each of them afterwards. Our conclusion is, that both methods yield noteworthy results, and each specializes in a different context.

[1]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Michael Goh Kah Ong,et al.  Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator , 2017, MIWAI.

[3]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[4]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[5]  Andrzej Majkowski,et al.  Emotion recognition using facial expressions , 2017, ICCS.

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Mohammad Teshnehlab,et al.  Face Recognition Using Convolutional Neural Network and Simple Logistic Classifier , 2014 .

[8]  Azar Fazel,et al.  Convolutional Neural Networks for Facial Expression Recognition , 2017, ArXiv.

[9]  Eric W. Gill,et al.  A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[10]  Dewi Yanti Liliana,et al.  Enhancing CNN with Preprocessing Stage in Automatic Emotion Recognition , 2017, ICCSCI.

[11]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[12]  Takeo Kanade,et al.  The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[13]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Andrea Cavallaro,et al.  Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  K. Pearson.  On the χ 2 Test of Goodness of Fit , 1922 .

[16]  Stefanos Zafeiriou,et al.  300 Faces In-The-Wild Challenge: database and results , 2016, Image Vis. Comput..

[17]  M. Student,et al.  Sobel Edge Detection Algorithm , 2013 .

[18]  Maja Pantic,et al.  Automatic Analysis of Facial Actions: A Survey , 2019, IEEE Transactions on Affective Computing.

[19]  Mu-Chun Su,et al.  A simple approach to facial expression recognition , 2007 .

[20]  C. Chibelushi,et al.  Facial Expression Recognition : A Brief Tutorial Overview , 2022 .

[21]  Liton Chandra Paul,et al.  Face recognition using Principal Component Analysis method , 2012 .