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Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Open Access Research Article

Edge aware directional coding (EADC) for face expression recognition

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pp. 979–989Vol. 46Issue 4-AMay 2025DOI: 10.47974/JIOS-1884XML
Received:
08 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1884
Pages:
979–989

Abstract

Face Expression Recognition (FER) has immense popularity in research community due to its widespread applicability in different applications. However, the currently available expression descriptors are suffering from several problems like noises, distortions and weak edges. To sort out these issues, this paper proposes an Edge Aware Directional Coding (EADC) that efficiently encodes the prominent expression related features. Initially, EADC finds out the gradients based on Kirsch Compass Mask in eight directions. Then EADC encode each pixel in facial image based on the Gradient Support of its neighbor pixels. Directional Edge Weight (DEW) is measured as support based on the gradient magnitude and gradient direction of edges passing through the neighbor pixels. EADC encode all the texture primitives such as corners and edges such that the recognition system can recognize expressions accurately independent of subjects. For experimental validation, JAFFE and CK+ datasets are used and the performance is measured through recognition accuracy. The obtained results show that the EADC outperforms all the earlier state-of-the-art methods. 

Keywords

Subject Classifications

Primary 68Secondary 97P80

References

[1] P. Weinberg and W. Gottwald, “Impulsive consumer buying as a result of emotions,” Journal of Business Research, vol. 10, no. 1, pp. 43–57 (1982).​
[2] T.-H.-S. Li, P.-H. Kuo, T.-N. Tsai, and P. C. Luan, “CNN and LSTM based facial expression analysis model for a humanoid robot,” IEEE Access, vol. 7, pp. 93998–94011 (2019).​
[3] M. R. Jeong and B. C. Ko, “Driver’s facial expression recognition in real-time for safe driving,” Sensors, vol. 18, no. 12, p. 4270, Dec. (2018).​
[4] M. S. Bouzakraoui, A. Sadiq, and N. Enneya, “A customer emotion recognition through facial expression using POEM descriptor and SVM classifier,” in Proc. 2nd Int. Conf. Big Data, Cloud Appl, pp. 1–6 ., 
Mar. (2017).​
[5] Y. S. Su, H. Y. Suen, and K. E. Hung, “Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews,” J. Real-Time Image Process., vol. 19, pp. 1011–1021, Jan. (2021).​
[6] M. Alhussein, “Automatic facial emotion recognition using Weber local descriptor for e-Healthcare system,” Cluster Comput., vol. 19, no. 1, pp. 99–108, Mar. (2016).​
[7] T. Ashwin and R. M. R. Guddeti, “Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks,” Educ. Inf. Technol., vol. 25, pp. 1–29, Mar. (2019).​
[8] J. Cai, Z. Meng, A. S. Khan, Z. Li, J. O’Reilly, and Y. Tong, “Island loss for learning discriminative features in facial expression recognition,” in Proc. 13th IEEE Int. Conf. Autom. Face Gesture Recognit. (FG), Xi’an, China, pp. 302–309, May (2018).​
[9] I. Kotsia and I. Pitas, “Facial expression recognition in image sequences using geometric deformation features and support vector machines,” IEEE Trans. Image Process., vol. 16, no. 1, pp. 172–187, Jan. (2007).​
[10] N. Bourbakis, A. Esposito, and D. Kavraki, “Extracting and associating meta-features for understanding people’s emotional behavior: Face and speech,” Cogn. Comput., vol. 3, no. 3, pp. 436–448 (2011).​
[11] C. Shan, S. Gong, and P. W. McOwan, “Facial expression recognition based on local binary patterns: A comprehensive study,” Image Vis. Comput., vol. 27, no. 6, pp. 803–816, Jun. (2009).​
[12] M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci., vol. 3, no. 1, pp. 71–86 (1991).​
[13] T. Jabid, M. H. Kabir, and O. Chae, “Robust facial expression recognition based on local directional pattern,” ETRI J., vol. 32, no. 5, pp. 784–794, Oct. (2010).​
[14] N. T. Cao, A. H. Ton-hat, and H. I. Choi, “Facial expression recognition based on local binary pattern features and support vector machine,” Int. J. Pattern Recognit. Artif. Intell., vol. 28, no. 6, 1456012 (2014).​
[15] N. Kauser and J. Sharma, “Facial expression recognition using LBP template of facial parts and multilayer neural network,” in Proc. Int. Conf. I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Palladam, India, pp. 445–449 (2017).​
[16] M. M. Goyani and N. Patel, “Recognition of facial expressions using local mean binary pattern,” Electron. Lett. Comput. Vis. Image Anal., vol. 16, no. 1, pp. 54–67 (2017).​
[17] A. Vijaya Lakshmi and P. Mohanaiah, “Gradient-based compact binary coding for facial expression recognition,” Int. J. Intell. Eng. Syst., vol. 14, no. 6, pp. 377–390 (2021).​
[18] S. Sammaiah and K. V. Rao, “A salient binary coding scheme for face and features of pixel and geometry,” IEEE Access, vol. 9, pp. 18876–18889 (2021).​
[19] J. Liu, Y. Feng, and H. Wang, “Facial expression recognition using pose-guided face alignment and discriminative features based on deep learning,” IEEE Access, vol. 9, pp. 69267–69277 (2021).​
[20] M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, “Coding facial expressions with Gabor wavelets,” in Proc. 3rd IEEE Int. Conf. Autom. Face Gesture Recognit., pp. 200–205, Apr. (1998).​
[21] P. Lucey, J. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended Cohn–Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Workshops, San.

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