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A Review On Various Techniques To Recognize Gesture Based Facial Expressions


Affiliations
1 Department of Computer Science, Central University of Jammu, Jammu, India
2 Department Computer Science, Central University of Jammu, Jammu, India
 

Nowadays, the Facial Expression Recognition (FER) system is a very important research topic in the fields of pattern recognition, Human-Computer Interaction (HCI) and Artificial Intelligence. The most essential steps in FER system are facial feature extraction and classification of expressions which improves the system performance for human computer interaction. This article reviews the feature extraction approaches such as Local Binary Pattern (LBP) (Divide the examined window into cells (e.g. 16x16 pixels for each cell).For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience).Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector. Optionally normalize the histogram. Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.), Spatio-Temporal feature using Local Zernike moment and Local Directional Position Pattern (LDPP). Also, the various combinations of Convolutional Neural Network (CNN) and Deep Neural Network (DNN) classifiers are reviewed. In addition, this paper makes a comparative study of various techniques used for analysing gesture based facial expressions and their corresponding performance.

Keywords

Facial Expression Recognition (FER), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Local Binary Pattern (LBP), Feature Extraction.
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  • Hsu, S. C., Huang, H. H., & Huang, C. L. (2017, April). “Facial Expression Recognition for Human-Robot Interaction.” In Robotic Computing (IRC), IEEE International Conference on (pp. 1-7). IEEE.

  • Căleanu, C. D. (2013, May). “Face expression recognition: A brief overview of the last decade.” In Applied Computational Intelligence and Informatics (SACI), 2013 IEEE 8th International Symposium on (pp. 157-161). IEEE.

  • Zhao, X., & Zhang, S. (2016). A Review on Facial Expression Recognition: Feature Extraction and Classification. IETE Technical Review, 33(5), 505-517.

  • Bernin, A., Müller, L., Ghose, S., von Luck, K., Grecos, C., Wang, Q., & Vogt, F. (2017, June). “Towards More Robust Automatic Facial Expression Recognition in Smart Environments.” In Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments (pp. 37-44). ACM.

  • Du, L., & Hu, H. (2017). “Modified classification and regression tree for facial expression recognition with using difference expression images.” Electronics Letters, 53(9), 590-592.

  • Xie, S., & Hu, H. (2017). “Facial expression recognition with FRR-CNN”. Electronics Letters, 53(4), 235-237.

  • Zhang, T., Zheng, W., Cui, Z., Zong, Y., Yan, J., & Yan, K. (2016). “A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition.” IEEE Transactions on Multimedia, 18(12), 2528-2536.

  • Zhang, K., Huang, Y., Du, Y., & Wang, L. (2017). “Facial expression recognition based on deep evolutional spatial-temporal networks”. IEEE Transactions on Image Processing, 26(9), 4193-4203.

  • Kumar, S., Bhuyan, M. K., & Chakraborty, B. K. (2016). “Extraction of informative regions of a face for facial expression recognition”. IET Computer Vision, 10(6), 567-576.

  • Uddin, M. Z., Hassan, M. M., Almogren, A., Alamri, A., Alrubaian, M., & Fortino, G. (2017). “Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief Network.” IEEE Access, 5, 4525-4536.

  • Kamarol, S. K. A., Jaward, M. H., Parkkinen, J., & Parthiban, R. (2016). “Spatiotemporal feature extraction for facial expression recognition. IET Image Processing”, 10(7), 534-541.

  • Hsieh, C. C., Hsih, M. H., Jiang, M. K., Cheng, Y. M., & Liang, E. H. (2016). “Effective semantic features for facial expressions recognition using svm. Multimedia Tools and Applications”, 75(11), 6663-6682.

  • Fan, X., & Tjahjadi, T. (2017). “A dynamic framework based on local Zernike moment and motion history image for facial expression recognition.” Pattern Recognition, 64, 399-406.

  • Yan, H. (2017). “Collaborative discriminative multi-metric learning for facial expression recognition in video”. Pattern Recognition.


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  • A Review On Various Techniques To Recognize Gesture Based Facial Expressions

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Authors

Rohini Mahajan
Department of Computer Science, Central University of Jammu, Jammu, India
Devanand Padha
Department Computer Science, Central University of Jammu, Jammu, India

Abstract


Nowadays, the Facial Expression Recognition (FER) system is a very important research topic in the fields of pattern recognition, Human-Computer Interaction (HCI) and Artificial Intelligence. The most essential steps in FER system are facial feature extraction and classification of expressions which improves the system performance for human computer interaction. This article reviews the feature extraction approaches such as Local Binary Pattern (LBP) (Divide the examined window into cells (e.g. 16x16 pixels for each cell).For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience).Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector. Optionally normalize the histogram. Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.), Spatio-Temporal feature using Local Zernike moment and Local Directional Position Pattern (LDPP). Also, the various combinations of Convolutional Neural Network (CNN) and Deep Neural Network (DNN) classifiers are reviewed. In addition, this paper makes a comparative study of various techniques used for analysing gesture based facial expressions and their corresponding performance.

Keywords


Facial Expression Recognition (FER), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Local Binary Pattern (LBP), Feature Extraction.

References