access icon free Grey Wolf optimisation-based feature selection and classification for facial emotion recognition

The channels used to convey the human emotions consider actions, behaviours, poses, facial expressions, and speech. An immense research has been carried out to analyse the relationship between the facial emotions and these channels. The goal of this study is to develop a system for Facial Emotion Recognition (FER) that can analyse the elemental facial expressions of human, such as normal, smile, sad, surprise, anger, fear, and disgust. The recognition process of the proposed FER system is categorised into four processes, namely pre-processing, feature extraction, feature selection, and classification. After preprocessing, scale invariant feature transform -based feature extraction method is used to extract the features from the facial point. Further, a meta-heuristic algorithm called Grey Wolf optimisation (GWO) is used to select the optimal features. Subsequently, GWO-based neural network (NN) is used to classify the emotions from the selected features. Moreover, an effective performance analysis of the proposed as well as the conventional methods such as convolutional neural network, NN-Levenberg–Marquardt, NN-Gradient Descent, NN-Evolutionary Algorithm, NN-firefly, and NN-Particle Swarm Optimisation is provided by evaluating few performance measures and thereby, the effectiveness of the proposed strategy over the conventional methods is validated.

Inspec keywords: neural nets; particle swarm optimisation; emotion recognition; evolutionary computation; face recognition; optimisation; feature extraction; gradient methods

Other keywords: optimal features; feature extraction method; FER system; scale invariant feature; recognition process; pre-processing; facial point; NN-particle swarm optimisation; Grey Wolf optimisation; feature selection; facial emotions; facial emotion recognition; elemental facial expressions; human emotions; fear

Subjects: Optimisation techniques; Image recognition; Other topics in statistics; Computer vision and image processing techniques; Optimisation techniques; Neural computing techniques

References

    1. 1)
      • 22. Hargreaves, A., Mothersill, O., Anderson, M., et al: ‘Detecting facial emotion recognition deficits in schizophrenia using dynamic stimuli of varying intensities’, Neurosci. Lett., 2016, 633, pp. 4754.
    2. 2)
      • 26. Pu, X., Fan, K., Chen, X., et al: ‘Facial expression recognition from image sequences using twofold random forest classifier’, Neurocomputing, 2015, 168, pp. 11731180.
    3. 3)
      • 31. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey Wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 4661.
    4. 4)
      • 12. Moeini, A., Faez, K., Sadeghi, H., et al: ‘2D facial expression recognition via 3D reconstruction and feature fusion’, J. Vis. Commun. Image Represent., 2016, 35, pp. 114.
    5. 5)
      • 16. Zhou, Q., Shafiq, R., Zhou, Y., et al: ‘Face recognition using dense SIFT feature alignment’, Chin. J. Electron., 2016, 25, (6), pp. 10341039.
    6. 6)
      • 27. Azeem, A., Sharif, M., Shah, J.H., et al: ‘Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction’, J. Appl. Res. Technol., 2015, 13, (3), pp. 402408.
    7. 7)
      • 37. Sánchez, D., Melin, P., Castillo, O.: ‘Optimization of modular granular neural networks using a firefly algorithm for human recognition’, Eng. Appl. Artif. Intell., 2017, 64, pp. 172186.
    8. 8)
      • 10. Neoh, S.C., Zhang, L., Mistry, K., et al: ‘Intelligent facial emotion recognition using a layered encoding cascade optimization model’, Appl. Soft Comput., 2015, 34, pp. 7293.
    9. 9)
      • 24. Lopes, A.T., Aguiar, E., Souza, A.F.D., et al: ‘Facial expression recognition with convolutional neural networks: coping with few data and the training sample order’, Pattern Recognit., 2017, 61, pp. 610628.
    10. 10)
      • 21. Zhang, L., Mistry, K., Neoh, S.C., et al: ‘Intelligent facial emotion recognition using moth-firefly optimization’, Knowl. Based Syst., 2016, 111, pp. 248267.
    11. 11)
      • 17. Gola, K.A., Shany-Ur, T., Pressman, P., et al: ‘A neural network underlying intentional emotional facial expression in neurodegenerative disease’, NeuroImage: Clin., 2017, 14, pp. 672678.
    12. 12)
      • 5. Brak, L.B., Abby, L., Richman, D.M., et al: ‘Facial emotion recognition among typically developing young children: a psychometric validation of a subset of NimStim stimuli’, Psychiatry Res., 2017, 249, pp. 109114.
    13. 13)
      • 33. Celika, O., Tekeb, A., Yildirima, H.B.: ‘The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean region of Turkey’, J. Clean Prod., 2016, 116, pp. 112.
    14. 14)
      • 45. Wang, S., Liu, Z., Wang, J., et al: ‘Exploiting multi-expression dependences for implicit multi-emotion video tagging’, Image Vis. Comput., 2014, 32, (10), pp. 682691.
    15. 15)
      • 15. Fan, X., Tjahjadi, T.: ‘A dynamic framework based on local zernike moment and motion history image for facial expression recognition’, Pattern Recognit., 2017, 64, pp. 399406.
    16. 16)
      • 29. Mirjalili, S., Saremi, S., Mirjalili, S.M., et al: ‘Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization’, Expert Syst. Appl., 2016, 47, pp. 106119.
    17. 17)
      • 8. Balas, B., Huynh, C., Saville, A., et al: ‘Orientation biases for facial emotion recognition during childhood and adulthood’, J. Exper. Child Psychol., 2015, 140, pp. 171183.
    18. 18)
      • 13. Meng, H., Berthouze, N.B., Deng, Y., et al: ‘Time-delay neural network for continuous emotional dimension prediction from facial expression sequences’, IEEE Trans. Cybern., 2016, 46, (4), pp. 916929.
    19. 19)
      • 32. Komaki, G.M., Kayvanfar, V.: ‘Grey Wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time’, J. Comput. Sci., 2015, 8, pp. 109120.
    20. 20)
      • 34. Yang, M., Liu, Y., You, Z.: ‘The Euclidean embedding learning based on convolutional neural network for stereo matching’, Neurocomputing, 2017, 267, pp. 195200.
    21. 21)
      • 20. Tang, X.W., Yu, M., Duan, W.W., et al: ‘Facial emotion recognition and alexithymia in Chinese male patients with deficit schizophrenia’, Psychiatry Res., 2016, 246, pp. 353359.
    22. 22)
      • 43. Ahmed, F.: ‘Gradient directional pattern: a robust feature descriptor for facial expression recognition’, Electron. Lett., 2012, 48, (19), pp. 12031204.
    23. 23)
      • 38. Lin, C.H.: ‘Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system’, ISA Trans., 2016, 64, pp. 405417.
    24. 24)
      • 40. Sun, Y., Akansu, A.N.: ‘Facial expression recognition with regional hidden Markov models’, Electron. Lett., 2014, 50, (9), pp. 671673.
    25. 25)
      • 44. Moeini, A., Faez, K., Moeini, H., et al: ‘Facial expression recognition using dual dictionary learning’, J. Vis. Commun. Image Represent., 2017, 45, pp. 2033.
    26. 26)
      • 2. Lenc, L., Kral, P.: ‘Automatic face recognition system based on the SIFT features’, Comput. Electr. Eng., 2015, 46, pp. 256272.
    27. 27)
      • 9. Lee, S.Y., Bang, M., Kim, K.R., et al: ‘Impaired facial emotion recognition in individuals at ultra-high risk for psychosis and with first-episode schizophrenia, and their associations with neurocognitive deficits and self-reported schizotypy’, Schizophrenia Res., 2015, 165, (1), pp. 6065.
    28. 28)
      • 47. Gaidhane, V.H., Singh, Y.V.H.V.: ‘Emotion recognition using eigenvalues and Levenberg–Marquardt algorithm-based classifier’, Sadhana, 2016, 41, (4), pp. 415423.
    29. 29)
      • 1. Zwick, J.C., Wolkenstein, L.: ‘Facial emotion recognition, theory of mind and the role of facial mimicry in depression’, J. Affective Disorders, 2017, 210, pp. 9099.
    30. 30)
      • 30. Song, X., Tang, L., Zhao, S., et al: ‘Grey Wolf optimizer for parameter estimation in surface waves’, Soil Dyn. Earthq. Eng., 2015, 75, pp. 147157.
    31. 31)
      • 25. Vinay, A., Kathiresan, G., Mundroy, D.A., et al: ‘Face recognition using filtered EOH-sift’, Procedia Comput. Sci., 2016, 79, pp. 543552.
    32. 32)
      • 18. Alqahtani, M.M.J.: ‘An investigation of emotional deficit and facial emotion recognition in traumatic brain injury: a neuropsychological study’, Postępy Psychiatrii I Neurologii, 2015, 24, (4), pp. 217224.
    33. 33)
      • 14. Russo, M., Mahon, K., Shanahan, M., et al: ‘The association between childhood trauma and facial emotion recognition in adults with bipolar disorder’, Psychiatry Res., 2015, 229, (3), pp. 771776.
    34. 34)
      • 3. Fasel, B.: ‘Robust face analysis using convolutional neural networks’. Object Recognition Supported by User Interaction for Service Robots, Switzerland, 2002, vol. 2, pp. 4043.
    35. 35)
      • 28. Boutorh, A., Guessoum, A.: ‘Complex diseases SNP selection and classification by hybrid association rule mining and artificial neural network – based evolutionary algorithms’, Eng. Appl. Artif. Intell., 2016, 51, pp. 5870.
    36. 36)
      • 36. Kobayashi, M.: ‘Gradient descent learning for quaternionic Hopfield neural networks’, Neurocomputing, 2017, 260, pp. 174179.
    37. 37)
      • 4. Theurel, A., Witt, A., Malsert, J., et al: ‘The integration of visual context information in facial emotion recognition in 5- to 15-year-olds’, J. Exper. Child Psychol., 2016, 150, pp. 252271.
    38. 38)
      • 11. Prado, C.E., Treeby, M.S., Crowe, S.F.: ‘Examining relationships between facial emotion recognition, self-control, and psychopathic traits in a non-clinical sample’, Personality Individual Differ., 2015, 80, pp. 2227.
    39. 39)
      • 39. Ozturk, S., Akdemir, B.: ‘Automatic leaf segmentation using grey wolf optimizer based neural network’. Electronics, Palanga, 2017, pp. 16.
    40. 40)
      • 7. Matamoros, A.H., Bonarini, A., Hernandez, E.E., et al: ‘Facial expression recognition with automatic segmentation of face regions using a fuzzy based classification approach’, Knowl.-Based Syst., 2016, 110, pp. 114.
    41. 41)
      • 42. Sonmez, E.B., Albayrak, S.: ‘Critical parameters of the sparse representation-based classifier’, IET Comput. Vis., 2013, 7, (6), pp. 500507.
    42. 42)
      • 23. Zhang, T., Zheng, W., Cui, Z., et al: ‘A deep neural network-driven feature learning method for multi-view facial expression recognition’, IEEE Trans. Multimed., 2016, 18, (12), pp. 25282536.
    43. 43)
      • 19. Rigon, A., Voss, M.W., Turkstra, L.S., et al: ‘Relationship between individual differences in functional connectivity and facial-emotion recognition abilities in adults with traumatic brain injury’, NeuroImage: Clin., 2017, 13, pp. 370377.
    44. 44)
      • 35. Jian, J., Jun, L., Hua, Z.X., et al: ‘Inversion of neural network Rayleigh wave dispersion based on LM algorithm’, Procedia Eng., 2011, 15, pp. 51265132.
    45. 45)
      • 6. Cruz, A.C., Bhanu, B., Thakoor, N.S.: ‘Vision and attention theory based sampling for continuous facial emotion recognition’, IEEE Trans. Affective Comput., 2014, 5, (4), pp. 418431.
    46. 46)
      • 41. Yi, J., Mao, X., Chen, L., et al: ‘Facial expression recognition considering individual differences in facial structure and texture’, IET Comput. Vis., 2014, 8, (5), pp. 429440.
    47. 47)
      • 46. Yu, K., Wang, Z., Zhuo, L., et al: ‘Learning realistic facial expressions from web images’, Pattern Recognit., 2013, 46, (8), pp. 21442155.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2017.0160
Loading

Related content

content/journals/10.1049/iet-bmt.2017.0160
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading