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Infant Attachment Prediction Using Vision and Audio Features in Mother-Infant Interaction

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12047))

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Abstract

Attachment is a deep and enduring emotional bond that connects one person to another across time and space. Our early attachment styles are established in childhood through the interaction between infants and caregivers. There are two attachment types, secure and insecure. The attachment experience affects personality development, particularly a sense of security, and research shows that it influences the ability to form stable relationships throughout life. It is also an important aspect of assessing the quality of parenting. Therefore, attachment has been widely studied in psychology research. It’s usually acquired by Ainsworth’s Strange Situation Assessment (SSA) through tedious observation. As far as we know, there is no computational method to predict infant attachment type. We try to use the Still-Face Paradigm (SFP) video and audio as input to predict attachment types through machine learning methods. In the present work, we recruited 64 infant-mother participants, collected videos of SFP when babies are 5–8 months of age and identified their attachment types including secure and insecure by SSA when those infants are almost 2 years old. For the visual part, we extract motion features and apply a RNN network with LSTM units model for classification. For the audio part, speech enhancement is conducted as data pre-processing, pitch frequency, short-time energy and Mel Frequency Cepstral Coefficient feature sequences are extracted. Then SVM is deployed to explore the patterns in it. The experiments show that our method is able to discriminate between the 2 classes of subjects with a good accuracy.

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References

  1. Bowlby, J.: Attachment theory and its therapeutic implications. Adolesc. Psychiatry 6, 5–33 (1978)

    Google Scholar 

  2. Firestone, L.: Disorganized Attachment

    Google Scholar 

  3. Braungart-rieker, J., Garwood, M., Powers, B., Wang, X.: Parental sensitivity, infant affect, and affect regulation: predictors of later attachment. Child Dev. 72, 252–270 (2001)

    Article  Google Scholar 

  4. Braungart-rieker, J., Zentall, S., Lickenbrock, D., Ekas, N., Oshio, T., Planalp, E.: Attachment in the making: mother and father sensitivity and infants’ responses during the still-face paradigm. J. Exp. Child Psychol. 125, 63–84 (2014)

    Article  Google Scholar 

  5. Tronick, E., Als, H., Adamson, L., Wise, S., Brazelton, T.: The infant’s response to entrapment between contradictory messages in face-to-face interaction. Pediatrics 62, 403–403 (1978)

    Google Scholar 

  6. Cohn, J.: Additional components of the still-face effect: commentary on Adamson and Frick. Infancy 4, 493–497 (2003)

    Article  Google Scholar 

  7. Ainsworth, M., Blehar, M., Waters, E., Wall, S.: Patterns of Attachment: A Psychological Study of the Strange Situation. Psychology Press, London (2015)

    Google Scholar 

  8. Ainsworth, M., Blehar, M., Waters, E., Wall, S.: Patterns of Attachment: Assessed in the Strange Situation and at Home. Erlbau, Hillsdale (1978)

    Google Scholar 

  9. Zeng, Z., et al.: Audio-visual affect recognition. IEEE Trans. Multimedia 9, 424–428 (2007)

    Article  Google Scholar 

  10. Qu, J., Leerkes, E.: Patterns of RSA and observed distress during the still-face paradigm predict later attachment, compliance and behavior problems: a person-centered approach. Dev. Psychobiol. 60, 707–721 (2018)

    Article  Google Scholar 

  11. Hammal, Z., Cohn, J., Messinger, D.: Head movement dynamics during play and perturbed mother-infant interaction. IEEE Trans. Affect. Comput. 6, 361–370 (2015)

    Article  Google Scholar 

  12. Egmose, I., et al.: Relations between automatically extracted motion features and the quality of mother-infant interactions at 4 and 13 months. Front. Psychol. 8, 2178 (2017)

    Article  Google Scholar 

  13. Messinger, D., Mahoor, M., Chow, S., Cohn, J.: Automated measurement of facial expression in infant-mother interaction: a pilot study. Infancy 14, 285–305 (2009)

    Article  Google Scholar 

  14. Cohen, D., et al.: Do parentese prosody and fathers’ involvement in interacting facilitate social interaction in infants who later develop autism? Plos One 8, e61402 (2013)

    Article  Google Scholar 

  15. Weisman, O., et al.: Dynamics of non-verbal vocalizations and hormones during father-infant interaction. IEEE Trans. Affect. Comput. 7, 337–345 (2015)

    Article  Google Scholar 

  16. Leclère, C., et al.: Interaction and behaviour imaging: a novel method to measure mother-infant interaction using video 3D reconstruction. Transl. Psychiatry 6, e816 (2016)

    Article  Google Scholar 

  17. Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Vis. Comput. 60, 4–21 (2017)

    Article  Google Scholar 

  18. Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos

    Google Scholar 

  19. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks

    Google Scholar 

  20. Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description

    Google Scholar 

  21. Zunino, A., et al.: Video gesture analysis for autism spectrum disorder detection

    Google Scholar 

  22. Elayadi, M., Kamel, M., Karray, F.: Survey on speech emotion recognition: features, classification schemes, and datasets. Pattern Recogn. 44, 572–587 (2011)

    Article  Google Scholar 

  23. Davis, S., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Sig. Process. 28, 357–366 (1980)

    Article  Google Scholar 

  24. Osmani, A., Hamidi, M., Chibani, A.: Machine learning approach for infant cry interpretation

    Google Scholar 

  25. Zhang, S., Zhang, S., Huang, T., Gao, W., Tian, Q.: Learning affective features with a hybrid deep model for audio-visual emotion recognition. IEEE Trans. Circuits Syst. Video Technol. 28, 3030–3043 (2017)

    Article  Google Scholar 

  26. Anbarjafari, G., Noroozi, F., Marjanovic, M., Njegus, A., Escalera, S.: Audio-visual emotion recognition in video clips

    Google Scholar 

  27. Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention. Arxiv Preprint Arxiv:1511.04119 (2015)

  28. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Arxiv Preprint Arxiv:1409.1556 (2014)

  29. Noroozi, F., Sapiński, T., Kamińska, D., Anbarjafari, G.: Vocal-based emotion recognition using random forests and decision tree. Int. J. Speech Technol. 20, 239–246 (2017)

    Article  Google Scholar 

  30. Park, S., Lee, J.: A fully convolutional neural network for speech enhancement. Arxiv Preprint Arxiv:1609.07132 (2016)

  31. Zeng, Z., Hu, Y., Fu, Y., Huang, T., Roisman, G., Wen, Z.: Audio-visual emotion recognition in adult attachment interview

    Google Scholar 

  32. Kamińska, D., Sapiński, T., Anbarjafari, G.: Efficiency of chosen speech descriptors in relation to emotion recognition. EURASIP J. Audio Speech Music Process. 2017, 3 (2017)

    Google Scholar 

  33. Noroozi, F., Marjanovic, M., Njegus, A., Escalera, S., Anbarjafari, G.: Audio-visual emotion recognition in video clips. IEEE Trans. Affect. Comput. 10, 60–75 (2017)

    Article  Google Scholar 

  34. Haq, S., Jackson, P.: Multimodal Emotion Recognition. IGI Global, Hershey (2011)

    Google Scholar 

  35. Rabiner, L.: On the use of autocorrelation analysis for pitch detection. IEEE Trans. Acoust. Speech Sig. Process. 25, 24–33 (1977)

    Article  Google Scholar 

  36. Chaudhry, R., Ran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions

    Google Scholar 

  37. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection

    Google Scholar 

  38. Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance

    Google Scholar 

  39. Wang, H., Schmid, C.: Action recognition with improved trajectories

    Google Scholar 

  40. Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)

    Article  Google Scholar 

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Acknowledgment

This work was supported by National Key R&D Program of China (2017YFB1002503).

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Authors and Affiliations

  1. Key Laboratory of Machine Perception, Peking University, Beijing, 100871, China

    Honggai Li, Jinshi Cui & Hongbin Zha

  2. School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China

    Li Wang

Authors
  1. Honggai Li
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  2. Jinshi Cui
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  3. Li Wang
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  4. Hongbin Zha
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Corresponding authors

Correspondence to Honggai Li or Jinshi Cui .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Li, H., Cui, J., Wang, L., Zha, H. (2020). Infant Attachment Prediction Using Vision and Audio Features in Mother-Infant Interaction. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_38

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  • DOI: https://doi.org/10.1007/978-3-030-41299-9_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41298-2

  • Online ISBN: 978-3-030-41299-9

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