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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31581–31603 | Cite as

Facial expression recognition for monitoring neurological disorders based on convolutional neural network

  • Gozde Yolcu
  • Ismail Oztel
  • Serap Kazan
  • Cemil Oz
  • Kannappan Palaniappan
  • Teresa E. Lever
  • Filiz BunyakEmail author
Article
  • 72 Downloads

Abstract

Facial expressions are a significant part of non-verbal communication. Recognizing facial expressions of people with neurological disorders is essential because these people may have lost a significant amount of their verbal communication ability. Such an assessment requires time consuming examination involving medical personnel, which can be quite challenging and expensive. Automated facial expression recognition systems that are low-cost and non-invasive can help experts detect neurological disorders. In this study, an automated facial expression recognition system is developed using a novel deep learning approach. The architecture consists of four-stage networks. The first, second and third networks segment the facial components which are essential for facial expression recognition. Owing to the three networks, an iconize facial image is obtained. The fourth network classifies facial expressions using raw facial images and iconize facial images. This four-stage method combines holistic facial information with local part-based features to achieve more robust facial expression recognition. Preliminary experimental results achieved 94.44% accuracy for facial expression recognition on RaFD database. The proposed system produced 5% improvement than the facial expression recognition system by using raw images. This study presents a quantitative, objective and non-invasive facial expression recognition system to help in the monitoring and diagnosis of neurological disorders influencing facial expressions.

Keywords

Facial component segmentation Facial expression recognition Convolutional neural network Deep learning 

Notes

Acknowledgements

Gozde Yolcu and Ismail Oztel have worked in this research while at University of Missouri-Columbia as visiting scholars and this study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK-BIDEB 2214/A) and The Sakarya University Scientific Research Projects Unit (Project number: 2015-50-02-039).

References

  1. 1.
    Adams D, Horsler K, Mount R, Oliver C (2015) Brief Report: A Longitudinal Study of Excessive Smiling and Laughing in Children with Angelman Syndrome. J Autism Dev Disord 45(8):2624–2627Google Scholar
  2. 2.
    Agarwal S, Santra B, Mukherjee DP (2018) Anubhav: recognizing emotions through facial expression. Vis Comput 34(2):177–191Google Scholar
  3. 3.
    Aifanti N, Delopoulos A (2014) Linear subspaces for facial expression recognition. Signal Process Image Commun 29(1):177–188Google Scholar
  4. 4.
    Aifanti N, Papachristou C, Delopoulos A (2010) The MUG facial expression database. In: 11th International Workshop on Image and Audio Analysis for Multimedia Interactive services, WIAMIS 2010, pp. 1–4Google Scholar
  5. 5.
    Aina S, Zhou M, Chambers JA, Phan RC (2014) A new spontaneous expression database and a study of classification-based expression analysis methods. In: 2014 22nd European Signal Processing Conference (EUSIPCO), pp. 2505–2509.Google Scholar
  6. 6.
    Ali G, Iqbal MA, Choi T-S (2016) Boosted NNE collections for multicultural facial expression recognition. Pattern Recogn 55:14–27Google Scholar
  7. 7.
    Alphonse AS, Dharma D (2018) Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition. Multimed Tools Appl 77(8):9455–9488Google Scholar
  8. 8.
    Aydogdu MF, Celik V, Demirci MF (2017) Comparison of Three Different CNN Architectures for Age Classification. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pp. 372–377Google Scholar
  9. 9.
    Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495Google Scholar
  10. 10.
    Baugh RF, Basura GJ, Ishii LE, Schwartz SR, Drumheller CM, Burkholder R, Deckard NA, Dawson C, Driscoll C, Gillespie MB, Gurgel RK, Halperin J, Khalid AN, Kumar KA, Micco A, Munsell D, Rosenbaum S, Vaughan W (2013) Clinical Practice Guideline. Otolaryngol Head Neck Surg 149(5):656–663Google Scholar
  11. 11.
    Ben Abdallah T, Guermazi R, Hammami M (2018) Facial-expression recognition based on a low-dimensional temporal feature space. Multimed Tools Appl 77(15):19455–19479Google Scholar
  12. 12.
    Bevilacqua V, D’Ambruoso D, Mandolino G, Suma M (2011) A new tool to support diagnosis of neurological disorders by means of facial expressions. In: 2011 IEEE International Symposium on Medical Measurements and Applications, pp. 544–549Google Scholar
  13. 13.
    Bijlstra G, Dotsch R (2011) FaceReader 4 emotion classification performance on images from the Radboud Faces DatabaseGoogle Scholar
  14. 14.
    Brewer R, Biotti F, Catmur C, Press C, Happé F, Cook R, Bird G (2016) Can Neurotypical Individuals Read Autistic Facial Expressions? Atypical Production of Emotional Facial Expressions in Autism Spectrum Disorders. Autism Res 9(2):262–271Google Scholar
  15. 15.
    Cha KH, Hadjiiski L, Samala RK, Chan H-P, Caoili EM, Cohan RH (2016) Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys 43(4):1882–1896Google Scholar
  16. 16.
    Chang J, Ryoo S (2018) Implementation of an improved facial emotion retrieval method in multimedia system. Multimed Tools Appl 77(4):5059–5065Google Scholar
  17. 17.
    Chen J, Xu R, Liu L (2018) Deep peak-neutral difference feature for facial expression recognition. Multimed Tools ApplGoogle Scholar
  18. 18.
    Cheng H-C, Cardone A, Krokos E, Stoica B, Faden A, Varshney A (2017) Deep-learning-assisted visualization for live-cell images. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 1377–1381Google Scholar
  19. 19.
    da Silva FAM, Pedrini H (2015) Effects of cultural characteristics on building an emotion classifier through facial expression analysis. Journal of Electronic Imaging 24(2):23015Google Scholar
  20. 20.
    Dantcheva A, Bilinski P, Nguyen HT, Broutart J-C, Bremond F (2017) Expression recognition for severely demented patients in music reminiscence-therapy. In 2017 25th European Signal Processing Conference (EUSIPCO), pp. 783–787Google Scholar
  21. 21.
    Dapogny A, Grossard C, Hun S, Serret S, Bourgeois J, Jean-Marie H, Foulon P, Ding H, Chen L, Dubuisson S, Grynszpan O, Cohen D, Bailly K (2018) JEMImE: A Serious Game to Teach Children with ASD How to Adequately Produce Facial Expressions. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 723–730Google Scholar
  22. 22.
    Dornaika F, Moujahid A, Raducanu B (2013) Facial expression recognition using tracked facial actions: Classifier performance analysis. Eng Appl Artif Intell 26(1):467–477Google Scholar
  23. 23.
    Edvinsson SE, Lundqvist L-O (2016) Prevalence of orofacial dysfunction in cerebral palsy and its association with gross motor function and manual ability. Dev Med Child Neurol 58(4):385–394Google Scholar
  24. 24.
    Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. J Pers Soc Psychol 17(2):124–129Google Scholar
  25. 25.
    Ekman P, Friesen WV (2002) Investigator’s Guide to the Facial Action Coding System (FACS)Google Scholar
  26. 26.
    Fathallah A, Abdi L, Douik A (2017) Facial Expression Recognition via Deep Learning. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 745–750.Google Scholar
  27. 27.
    Fernandez-Duque D, Black SE (2005) Impaired recognition of negative facial emotions in patients with frontotemporal dementia. Neuropsychologia 43(11):1673–1687Google Scholar
  28. 28.
    Ghimire D, Jeong S, Yoon S, Choi J, Lee J (2015) Facial expression recognition based on region specific appearance and geometric features. In: 2015 Tenth International Conference on Digital Information Management (ICDIM), pp. 142–147Google Scholar
  29. 29.
    Ghimire D, Lee J (2013) Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines. Sensors 13(6):7714–7734Google Scholar
  30. 30.
    Gola KA, Shany-Ur T, Pressman P, Sulman I, Galeana E, Paulsen H, Nguyen L, Wu T, Adhimoolam B, Poorzand P, Miller BL, Rankin KP (2017) A neural network underlying intentional emotional facial expression in neurodegenerative disease. NeuroImage: Clinical 14:672–678Google Scholar
  31. 31.
    Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, CambridgezbMATHGoogle Scholar
  32. 32.
    Guha T, Yang Z, Ramakrishna A, Grossman RB, Hedley D, Lee S, Narayanan SS (2015) On quantifying facial expression-related atypicality of children with Autism Spectrum Disorder. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 803–807Google Scholar
  33. 33.
    Guo M, Hou X, Ma Y (2017) Facial expression recognition using ELBP based on covariance matrix transform in KLT. Multimed Tools Appl:2995–3010Google Scholar
  34. 34.
    Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXivGoogle Scholar
  35. 35.
    Hosseini S, Lee SH, Kwon HJ, Il Koo H, Cho NI (2018) Age and gender classification using wide convolutional neural network and Gabor filter. in 2018 International Workshop on Advanced Image Technology (IWAIT), pp. 1–3.Google Scholar
  36. 36.
    Ilbeygi M, Shah-Hosseini H (2012) A novel fuzzy facial expression recognition system based on facial feature extraction from color face images. Eng Appl Artif Intell 25(1):130–146Google Scholar
  37. 37.
    Jia S, Lansdall-Welfare T, Cristianini N (2016) Gender Classification by Deep Learning on Millions of Weakly Labelled Images. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 462–467Google Scholar
  38. 38.
    Khan SA, Hussain A, Usman M (2018) Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features. Multimed Tools Appl 77(1):1133–1165Google Scholar
  39. 39.
    Kohler CG (2005) Emotion-Discrimination Deficits in Mild Alzheimer Disease. Am J Geriatr Psychiatr 13(11):926–933Google Scholar
  40. 40.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. Adv Neural Inf Proces Syst:1–9Google Scholar
  41. 41.
    Langner O, Dotsch R, Bijlstra G, Wigboldus DHJ, Hawk ST, van Knippenberg A (2010) Presentation and validation of the Radboud Faces Database. Cognit Emot 24(8):1377–1388Google Scholar
  42. 42.
    Lecun Y (1989) Generalization and network design strategies. In: Pfeifer R, Schreter Z, Fogelman F, Steels L (eds) Connectionism in perspective. Elsevier, ZurichGoogle Scholar
  43. 43.
    Li Z, Zhang Q, Duan X, Wang C, Shi Y (2018) New semantic descriptor construction for facial expression recognition based on axiomatic fuzzy set. Multimed Tools Appl 77(10):11775–11805Google Scholar
  44. 44.
    Lin J, Chen Y, Wen H, Yang Z, Zeng J (2017) Weakness of Eye Closure with Central Facial Paralysis after Unilateral Hemispheric Stroke Predicts a Worse Outcome. J Stroke Cerebrovasc Dis 26(4):834–841Google Scholar
  45. 45.
    Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1015–1018Google Scholar
  46. 46.
    Livingstone SR, Vezer E, McGarry LM, Lang AE, Russo FA (2016) Deficits in the Mimicry of Facial Expressions in Parkinson’s Disease. Front Psychol 7Google Scholar
  47. 47.
    Lopes AT, de Aguiar E, De Souza AF, Oliveira-Santos T (2017) Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order. Pattern Recogn 61:610–628Google Scholar
  48. 48.
    Lou Y, Fu G, Jiang Z, Men A, Zhou Y (2017) PT-NET: Improve object and face detection via a pre-trained CNN model. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1280–1284.Google Scholar
  49. 49.
    Luus FPS, Salmon BP, van den Bergh F, Maharaj BTJ (2015) Multiview Deep Learning for Land-Use Classification. IEEE Geosci Remote Sens Lett 12(12):2448–2452Google Scholar
  50. 50.
    Mandache D, Dalimier E, Durkin JR, Boceara C, Olivo-Marin J-C, Meas-Yedid V (2018) Basal cell carcinoma detection in full field OCT images using convolutional neural networks. in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 784–787.Google Scholar
  51. 51.
    Matsugu M, Mori K, Mitari Y, Kaneda Y (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5–6):555–559Google Scholar
  52. 52.
    Mehrabian A (1968) Some referents and measures of nonverbal behavior. Behav Res Methods Instrum 1(6):203–207Google Scholar
  53. 53.
    Nair V, Hinton GE (2010) Rectified Linear Units Improve Restricted Boltzmann Machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, pp. 807–814Google Scholar
  54. 54.
    Nigam S, Singh R, Misra AK (2018) Efficient facial expression recognition using histogram of oriented gradients in wavelet domain. Multimed Tools ApplGoogle Scholar
  55. 55.
    Oztel I, Yolcu G, Ersoy I, White T, Bunyak F (2017) Mitochondria segmentation in electron microscopy volumes using deep convolutional neural network. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1195–1200.Google Scholar
  56. 56.
    Oztel I, Yolcu G, Ersoy I, White TA, Bunyak F (2018) Deep learning approaches in electron microscopy imaging for mitochondria segmentation. International Journal of Data Mining and Bioinformatics 21(2):91Google Scholar
  57. 57.
    Oztel I, Yolcu G, Oz C, Kazan S, Bunyak F (2018) iFER: facial expression recognition using automatically selected geometric eye and eyebrow features. Journal of Electronic Imaging 27(2):1Google Scholar
  58. 58.
    Pitaloka DA, Wulandari A, Basaruddin T, Liliana DY (2017) Enhancing CNN with Preprocessing Stage in Automatic Emotion Recognition. Procedia Computer Science 116:523–529Google Scholar
  59. 59.
    Pons G, Masip D (2017) Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis. IEEE Trans Affect Comput:1–1Google Scholar
  60. 60.
    Qin X, Zhou Y, He Z, Wang Y, Tang Z (2017) A Faster R-CNN Based Method for Comic Characters Face Detection. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1074–1080Google Scholar
  61. 61.
    Rao Q, Qu X, Mao Q, Zhan Y (2015) Multi-pose facial expression recognition based on SURF boosting. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 630–635Google Scholar
  62. 62.
    Ricciardi L, Visco-Comandini F, Erro R, Morgante F, Bologna M, Fasano A, Ricciardi D, Edwards MJ, Kilner J (2017) Facial Emotion Recognition and Expression in Parkinson’s Disease: An Emotional Mirror Mechanism? PLoS One 12(1):e0169110Google Scholar
  63. 63.
    S. C. Face++ (2017) Face++ Cognitive Services. Available: https://www.faceplusplus.com/. Accessed: 12 Nov 2017
  64. 64.
    Saha P, Bhattacharjee D, De BK, Nasipuri M (2018) Facial component-based blended facial expressions generation from static neutral face images. Multimed Tools Appl 77(15):20177–20206Google Scholar
  65. 65.
    Shelhamer E, Long J, Darrell T (2016) Fully Convolutional Networks for Semantic Segmentation. Cognit Emot 24(8):1377–1388Google Scholar
  66. 66.
    Shpilman A, Boikiy D, Polyakova M, Kudenko D, Burakov A, Nadezhdina E (2017) Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1–6.Google Scholar
  67. 67.
    Simonyan K, Zisserman A (2014) Very Deep Convolutional Networks for Large-Scale Image RecognitionGoogle Scholar
  68. 68.
    Singh S, Srivastava A, Mi L, Chen K, Wang Y, Caselli RJ, Goradia D, Reiman EM (2017) Deep-learning-based classification of FDG-PET data for Alzheimer’s disease categories. In: 13th International Conference on Medical Information Processing and Analysis, p. 84.Google Scholar
  69. 69.
    Socher R, Huval B, Bhat B, Manning CD, Ng AY (2012) Convolutional-recursive Deep Learning for 3D Object Classification. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, pp. 656–664Google Scholar
  70. 70.
    Sultan Zia M, Hussain M, Arfan Jaffar M (2018) A novel spontaneous facial expression recognition using dynamically weighted majority voting based ensemble classifier. Multimed Tools Appl 77(19):25537–25567Google Scholar
  71. 71.
    Sun X, Wu P, Hoi SCH (2018) Face detection using deep learning: An improved faster RCNN approach. Neurocomputing 299:42–50Google Scholar
  72. 72.
    Thevenot J, Lopez MB, Hadid A (2018) A Survey on Computer Vision for Assistive Medical Diagnosis From Faces. IEEE Journal of Biomedical and Health Informatics 22(5):1497–1511Google Scholar
  73. 73.
    Uddin MZ, Khaksar W, Torresen J (2017) Facial Expression Recognition Using Salient Features and Convolutional Neural Network. IEEE Access 5:26146–26161Google Scholar
  74. 74.
    Venturelli M, Borghi G, Vezzani R, Cucchiara R (2018) Deep Head Pose Estimation from Depth Data for In-Car Automotive Applications. In: Understanding Human Activities Through 3D Sensors, pp. 74–85.Google Scholar
  75. 75.
    Viola P, Jones MJ (2004) Robust Real-Time Face Detection. Int J Comput Vis 57(2):137–154Google Scholar
  76. 76.
    Wei B, Sun X, Ren X, Xu J (2017) Minimal Effort Back Propagation for Convolutional Neural Networks. Computing Research RepositoryGoogle Scholar
  77. 77.
    Wu C, Huang C, Chen H (2018) Expression recognition using semantic information and local texture features. Multimed Tools Appl 77(9):11575–11588Google Scholar
  78. 78.
    Wu B-F, Lin C-H (2018) Adaptive Feature Mapping for Customizing Deep Learning Based Facial Expression Recognition Model. IEEE Access 6:12451–12461Google Scholar
  79. 79.
    Xie X, Lam K-M (2009) Facial expression recognition based on shape and texture. Pattern Recogn 42(5):1003–1011Google Scholar
  80. 80.
    Xie W, Shen L, Yang M, Jiang J (2018) Facial expression synthesis with direction field preservation based mesh deformation and lighting fitting based wrinkle mapping. Multimed Tools Appl 77(6):7565–7593Google Scholar
  81. 81.
    Yolcu G, Oztel I, Kazan S, Oz C, Palaniappan K, Lever TE, Bunyak F (2017) Deep learning-based facial expression recognition for monitoring neurological disorders. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1652–1657Google Scholar
  82. 82.
    Yuvaraj R, Murugappan M, Sundaraj K (2012) Methods and approaches on emotions recognition in neurodegenerative disorders: A review. In 2012 IEEE Symposium on Industrial Electronics and Applications, pp. 287–292Google Scholar
  83. 83.
    Zhang H, Wang K, Tian Y, Gou C, Wang F-Y (2018) MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection. IEEE Trans Veh Technol:1–1Google Scholar
  84. 84.
    Zhong L, Liu Q, Yang P, Liu B, Huang J, Metaxas DN (2012) Learning active facial patches for expression analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2562–2569Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Gozde Yolcu
    • 1
    • 2
  • Ismail Oztel
    • 1
    • 2
  • Serap Kazan
    • 1
  • Cemil Oz
    • 1
  • Kannappan Palaniappan
    • 2
  • Teresa E. Lever
    • 3
  • Filiz Bunyak
    • 2
    Email author
  1. 1.Department of Computer EngineeringSakarya UniversitySerdivanTurkey
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaUSA
  3. 3.Department of OtolaryngologyUniversity of MissouriColumbiaUSA

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