Abstract
In the way of communication, facial expression act as non-verbal communication and play an important role in social interaction by providing some contextual information. Facial expressions also express human’s inner emotional state, which is very effective for communication with the actual emotions. In this paper, an algorithm has been proposed to detect the face and facial parts more accurate to the Viola – John’s algorithm, and a fast-tracking algorithm for face tracking in real-time scenarios. The fusion of the facial features is used for feature extraction and comparative work on the several classifiers has been presented. In this approach, the images were acquired and seven significant facial parts from the image were cropped, then extract and store the features of several facial expressions. Finally, the expressions in the images were recognized using the classifiers. The algorithm was tested on four kinds of database and achieved accurate performance through the designed system.
Similar content being viewed by others
References
Ahmed M, Laskar RH (2019) Eye detection and localization in a facial image based on the partial geometric shape of iris and eyelid under practical scenarios. J Electron Imaging 28(3):033009. https://doi.org/10.1117/1.JEI.28.3.033009
Bartlett MS, Littleworth G, Fasel I, Movellan JR (2003) Real-time face detection and facial expression recognition: development and applications to human-computer interaction. In: 2003 conference on computer vision and pattern recognition workshop, vol. 5, pp. 53–53. IEEE
Bartlett MS, Littleworth G, Fasel I, Movellan R (2003) Real-time face detection and facial expression recognition: development and application to human-computer interaction. In: Proceedings CVPR workshop on computer vision and pattern recognition for human-computer interaction, vol 5
Buciu I, Pitas I (2004) Application of non-negative and local non negative matrix factorization to facial expression recognition. In: Proceeding of ICPR, pp 288–291, Cambridge, UK, Aug. 23–26, 2004
Chen J, Chen Z, Chi Z, Hong F (2014) Facial expression recognition based on facial components detection and hog features. International workshops on electrical and computer engineering subfields, pp 884–888
Chibelushi CC, Bourel F (2003) Facial expression recognition: a brief tutorial overview. CVonline: On-Line Compendium of Computer Vision 9
CK+ database: http://www.consortium.ri.cmu.edu/ckagree/. Accessed 10 June 2017
Cristinacce D, Cootes TF, Scott IM (2004) A multi-stage approach to facial feature detection. In BMVC, vol 1, pp 277–286
Dumas M (2001) Emotional expression recognition using support vector machines. In: Proceedings of international conference on multimodal interfaces
Ekman P, Sorenson ER, Friesen WV (1969) Pancultural elements in Facial displays of emotion. Science 164(3875):86–88
Ekweariri AN, Yurtkan K (2017) Facial expression recognition using enhanced local binary patterns. In: 2017 9th international conference on Computational Intelligence and Communication Networks (CICN), pp 43–47. IEEE
El Maghraby A, Abdalla M, Enany O, El Nahas MY (2013) Hybrid face detection system using a combination of Viola-Jones method and skin detection. Int J Comput Appl 71(6):15–22
Feng X, Pietikainen M, Hadid A (2005) Facial expression recognition with local binary patterns and linear programming. Pattern Recognition And Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii 15(2):546. https://zenodo.org/record/3451524
JAFFE database: http://www.kasrl.org/jaffe.html/. Accessed 10 June 2017
Joho H, Staiano J, Sebe N, Jose JM (2011) Looking at the viewer:analyzing facial activity to detect personal highlights of multimedia contents. Multimed Tools Appl 51(2):505–523
Kasim S, Hassan R, Zaini NH, Ahmad AS, Ramli AA, Saedudin RR (2017) A study on facial expression recognition using local binary pattern. Int J Adv Sci EngInf Technol 7(5):1621–1626
Kotsia I, Pitas I (2007) Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Trans Image Process 16(1):172–187
Kotsia I, Buciu I, Pitas I (July 2008) An analysis of facial expression recognition under partial facial image occlusion. Image Vis Comput 26(7):1052–1067
Kumari J, Rajesh R, Pooja KM (2015) Facial expression recognition: a survey. Proc Comput Sci 58:486–491
Lemaire P, Amor BB, Ardabilian M, Chen L, Daoudi M (2011) Fully automatic 3D facial expression recognition using a region-based approach. In: Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, pp 53–58. ACM
Marcolin F et al (2017) 3D geometry-based automatic landmark localization in the presence of facial occlusions. Multimed Tools Appl 77:1–29
Mehrabian A (1968) Communication without words. Psychol Today 2(4):53–56
Michel P, Kaliouby R (2003) Real-time facial expression recognition in video using support vector machines. In: Proceedings ogf 5th International Conference of Multimodal Interfaces, Vancouver, BC, Canada, pp 258–264
MMI database: https://mmifacedb.eu/. Accessed 10 June 2017
Moore S, Bowden R (2011) Local binary patterns for multi-view facial expression recognition. Comput Vis Image Underst 115(4):541–558
Murthy GRS, Jadon RS (2009) Effectiveness of Eigenspaces for facial expressions recognition. Int J Comput Theory Eng 1(5):638–642
Pantic M, Patras I (2006) Dynamics of facial expression: recognition of facial actions and their temporal segments form face profile image sequences. IEEE Trans Syst Man Cybern B 36(2):433–449
Pantic M, Rothkrantz JM (2004) Facial action recognition for facial expression analysis from static face images. IEEE Trans Syst Man arid Cybern B 34(3):1449–1461
Punitha A, Geetha MK (2013) Texture-based emotion recognition from facial expressions using a support vector machine. Int J Comput Appl
Sandbach G, Zafeiriou S, Pantic M, Yin L (2012) Static and dynamic 3D facial expression recognition: a comprehensive survey. Image Vis Comput 30(10):683–697
Sarode N, Bhatia S (2010) Facial expression recognition. Int J Comput Sci Eng 2(5):1552–1557
Sawardekara S, Prof. Sowmiya Raksha Naik (2008) Facial expression recognition using efficient LBP and CNN,e-ISSN: 2395-0056, Volume: 05(June 2018), p-ISSN: 2395-0072
Sobottka K, Pitas I (1996) Face localization and facial feature extraction based on shape and color information. In: Proceedings of 3rd IEEE international conference on image processing, vol 3, pp 483–486. IEEE
The LNMIIT database: http://anshyadav.cf/the-lnmiit-datast/. Accessed 10 June 2017
Tian Y, Kanade T, Cohn J (2001) Recognizing action units for facial expression analysis. IEEE Trans Pattern Analysis Mach Intell 23(2):97–115
Tornincasa S et al (2019) 3D facial action units and expression recognition using a crisp logic. Comput Aided Des Appl 16(2):256–268
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol 1, pp I-I. IEEE
Vupputuri A, Meher S (2015) Facial expression recognition using local binary patterns and kullback leibler divergence. In: International Conference on Communications and Signal Processing (ICCSP), pp 0349–0353. IEEE
Wang J, Yin L (2007) Static topographic modeling for facial expression recognition and analysis. Comput Vis Image Underst 108:19–34
Yadav KS, Singha J, Laskar RH (2019) Facial expression recognition using facial features detection using the fusion of classifiers in the real-time scenario. In: Proceedings of 4th IEEE international conference on Information Systems and Computer Networks (ISCON)
Yang S, Luo P, Loy C-C, Tang X (2015) From facial parts responses to face detection: A deep learning approach. In: Proceedings of the IEEE international conference on computer vision, pp 3676–3684
Yang X, Li M, Zhao S (2017) Facial expression pecognition algorithm based on CNN and LBP feature fusion. In: Proceedings of the 2017 international conference on robotics and artificial intelligence, pp 33–38. ACM
Zeng Z, Pantic M, Roisman GI, Huang TS (2009) A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39–58
Zhang Z, Lyons M, Schuster M, Akamatsu S (1998) Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron. In: Proceedings third IEEE international conference on automatic face and gesture recognition, pp 454–459. IEEE
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yadav, K.S., Singha, J. Facial expression recognition using modified Viola-John’s algorithm and KNN classifier. Multimed Tools Appl 79, 13089–13107 (2020). https://doi.org/10.1007/s11042-019-08443-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-08443-x