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

摘要

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.