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International Journal of Sensors, Wireless Communications and Control

Author(s): Soumya Ranjan Mohanta and Karan Veer*

DOI: 10.2174/2210327913666230216151810

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Facial Emotion Recognition using Wavelet Analysis and Histogram of Oriented Gradients of Image

Page: [18 - 28] Pages: 11

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Abstract

Background: A number of disciplines, including security, healthcare, and human-machine interactions, have presented and used techniques for emotion recognition based on facial expressions.

Objective: To increase computer prediction, researchers are advancing the methods for deciphering code and extracting facial emotions.

Methods: The contamination of the image with noise, which alters the features of the images and ultimately impacts the accuracy of the system, is one of the major issues in this sector. Thus, noise should be eliminated or diminished. The wavelet transform approach is used in this study to denoise the images before categorization. The classification accuracies for original images are also obtained to analyze the effect of denoising on the classification accuracy of the facial expression images.

Results and Conclusion: Three machine learning approaches, support vector machine, k-nearest neighbor, and naive bayes, are utilized to classify the emotions in this instance. The feature employed is the histogram of directional gradients of images. The classification results are obtained and the effect of denoising on the classification accuracy of the facial expression images is analyzed. Also, our best-obtained result for the wavelet transform method is compared with other wavelet transform-based facial emotion recognition techniques. And our result is found to be promising.

Keywords: Wavelet transform, thresholding, decomposition, symlets, coiflets, daubechies, wavelet.

Graphical Abstract

[1]
Halder R, Sengupta S, Pal A, Ghosh S, Kundu D. Real time facial emotion recognition based on image processing and machine learning. Int J Comput Appl 2016; 139(11): 16-9.
[http://dx.doi.org/10.5120/ijca2016908707]
[2]
Mellouk W, Handouzi W. Facial emotion recognition using deep learning: review and insights. Procedia Comput Sci 2020; 175: 689-94.
[http://dx.doi.org/10.1016/j.procs.2020.07.101]
[3]
Upadhyay V, Kotak D. A review on different facial feature extrac- tion methods for face emotions recognition system. IEEE Fourth International Conference on Inventive Systems and Control. 2020 Jan 8-10; Coimbatore, India: IEEE 15-9.
[http://dx.doi.org/10.1109/ICISC47916.2020.9171172]
[4]
Gurevich IB, Yashina VV. Descriptive models of information transformation processes in image analysis. Pattern Recognit Image Anal 2021; 31(3): 402-20.
[http://dx.doi.org/10.1134/S105466182103010X]
[5]
Bijalwan A, Goyal A, Sethi N. Wavelet transform based image denoise using threshold approaches. Int J Eng Adv Technol 2012; 1.https://www.ijeat.org/wp-content/uploads/papers/v1i5/E0477061512.pdf
[6]
Koranga P, Singh G, Verma D. Image denoising using new proposed method based on wavelet transform for different wavelet families. IACSIT Int J Eng Technol 2017; 9(2): 1382-9.
[http://dx.doi.org/10.21817/ijet/2017/v9i2/170902287]
[7]
Chaudhari VA, Lade S. A review on image denoising techniques using wavelet transform methods. Int J Recent Develop Eng Technol 2014; 3: 37-41.
[8]
Singh Reshu. 2D-DWT: A brief intro” medium. 2020. Available from: https://medium.com/image-vision/2d-dwt-a-brief-intro-89e9ef1698e3
[9]
Adi PW, Astuti YP, Subhiyakto ER. Feature image watermarking based on Bicubic interpolation of wavelet coefficients using CRT. CommIT (Communication and Information Technology) 2017; 11(2): 93-9.
[http://dx.doi.org/10.21512/commit.v11i2.3870]
[10]
Othman G, Zeebaree DQ. The applications of discrete wavelet transform in image processing: A review. J Soft Comput Data Min 2020; 01(2): 31-43.
[11]
German-Sallo Z. Nonlinear wavelet denoising of data signals. UbiCC J 2011; 6: 895-900.
[12]
wbmpen. Available from: https://www.mathworks.com/help/wave-let/ref/wbmpen.html
[13]
Wavelet Families. Available from: https://in.mathworks.com/help/wavelet/ug/wavelet-families-additional-discussion.html
[14]
Goel N, Singh G. Study of wavelet functions of discrete wavelet transformation in image watermarking. Int J Eng Sci 2016; 17: 154-60.
[15]
Singh A. Feature Engineering for Images: A valuable introduction to the hog feature descriptor” Analytics Vidhya, 2019. Feature Descriptor | Hog Descriptor Tutorial 2019.
[16]
ML - Support Vector Machine (SVM). Tutorialspoint. Available from: http://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_support_vector_machine.htm
[17]
Dong Shi. Multi class SVM algorithm with active learning for network traffic classification. In: Lin B, Ed. Expert Systems with Applications. Amsteradam: Elsevier 2021; 176: p. 114885.
[http://dx.doi.org/10.1016/j.eswa.2021.114885]
[18]
Support Vector Machine Algorithm. java T point.. Available from: https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm
[19]
Bhambhu L, Srivastava D. Data classification using support vector machine. J Theor Appl Inf Technol 2010; 12: 1-7.
[20]
Salunkhe V. Support Vector Machine (SVM) In medium. 2021. Available from: https://medium.com/@viveksalunkhe80/support-vector-machine-svm-88f360ff5f38
[21]
Navlani A. KNN Classification using Scikit-learn datacamp. 2018. Available from: www.datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn
[22]
KNN Algorithm - Finding Nearest Neighbors” tutorialspoint KNN Algorithm. Finding Nearest Neighbors 2018. https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm
[23]
K-Nearest Neighbor(KNN) Algorithm for Machine Learning. java T point 2011. Available from: http://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning
[24]
Sun J, Sun J, Du W, Shi N. A survey of kNN algorithm. ResearchGate 2018; 1(1)
[http://dx.doi.org/10.18063/ieac.v1i1.770]
[25]
Lee JM. An efficient kNN algorithm. KIPS Transactions:PartB 2004; 11B(7): 849-54.
[http://dx.doi.org/10.3745/KIPSTB.2004.11B.7.849]
[26]
Flores-Juarez UA, Álvarez-Cedillo JA, Álvarez-Sánchez T. Two facial emotion detection based on naive Bayesian classifier. J Theor Appl Inf Technol 2021; 99: 5888-97.
[27]
Berrar D. Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. 2018; 403.
[http://dx.doi.org/10.1016/B978-0-12-809633-8.20473-1]
[28]
Sunil . 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R” Analytics Vidya. 2017. Analytics Vidya.
[29]
Naïve Bayes Classifier Algorithm. javaTpoint Available from: http://www.javatpoint.com/machine-learning-naive-bayes-classifier
[30]
Qayyum H, Majid M, Anwar SM, Khan B. Facial expression recognition using stationary wavelet transform features. Math Probl Eng 2017; 2017: 1-9.
[http://dx.doi.org/10.1155/2017/9854050]
[31]
Daqrouq Khaled, Balamesh Ahmed, Khalaf Emad. Efficient wavelet entropy-based face recognition system. J Eng Res. 2022.
[http://dx.doi.org/10.36909/jer.15971]
[32]
Kar NB, Babu KS. Facial expression recognition using 2d stationary wavelet transform and gray-level co-occurrence matrix. Proceedings of the 2018 International Conference on Control and Computer Vision. 13-7.
[http://dx.doi.org/10.1145/3232651.3232664]
[33]
Nain Neeta, Kumar Akshay, Mohapatra Amlesh Kumar, Das Ratan, Kumar Ashok. Face recognition using LDA with wavelet transform approach. Int J Inform Technol 2011; 4: 603-7.
[34]
Amarnatha SC, Murali KK, Rasool RK. Adaptive facial expression identification using PCA and wavelet transform. Indian J Sci Technol 2016; 9(39): 1-5.
[http://dx.doi.org/10.17485/ijst/2016/v9i39/100789]
[35]
Siddiqi MH, Lee S. Human facial expression recognition using wavelet transform and hidden markov model. International Workshop on Ambient Assisted Living. 112-9.
[http://dx.doi.org/10.1007/978-3-319-03092-0_17]
[36]
Saranya S, Saravanyah V, Balamurugan R, Student PG. Facial expression recognition using daubechies wavelet PCA for authentication. Int J Eng Sci 2014; 3(3)
[37]
Akram ADN. Facial expression recognition using wavelet transform and local binary pattern. IIEE 2nd International Conference for Convergence in Technology (I2CT). 338-42.
[http://dx.doi.org/10.1109/I2CT.2017.8226147]
[38]
Patil SM, Kasturiwala SB, Dahad SO, Jadhav CD. Daubechies wavelet tool: Application for human face recognition. Int J Eng Sci Technol 2011; 3(3) [https://www.researchgate.net/publication/50984920_Daubechies_Wavelet_Tool_Application_For_Human_Face_Recognition]
[39]
Abidin Z, Alamsyah A. Wavelet based approach for facial expression recognition. Int J Adv Intell Inform 2015; 1(1): 7-14.
[http://dx.doi.org/10.26555/ijain.v1i1.7]
[40]
Soumya M, Veer K. Trends and challenges of image analysis in facial emotion recognition: A Review. Netw Model Anal Health Inform Bioinform 2022; 11: 1-16.
[41]
Pooja , SK Pahuja, Veer Karan, et al. Wavelet denoising in a highly secure covert communication. Int J Sensors Wirel Commun Control 2020; 10: 139-43.
[42]
Sharma T, Veer K. Wavelet analysis-based evaluation of electromyogram signal using human machine cooperation. Int J Med Eng Inform 2016; 8(4): 317-28.
[http://dx.doi.org/10.1504/IJMEI.2016.079358]