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A new feature extraction technique for human facial expression recognition systems using depth camera


Siddiqi, MH and Ali, R and Kang, BH and Lee, S, A new feature extraction technique for human facial expression recognition systems using depth camera, Lecture Notes in Computer Science 8868: Proceedings of the 6th International Work-Conference, IWAAL 2014, 2-5 December 2014, Belfast, UK, pp. 131-138. ISSN 0302-9743 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 Springer International Publishing Switzerland

DOI: doi:10.1007/978-3-319-13105-4_21


The analysis of facial expressions in telemedicine and healthcare plays a significant role in providing sufficient information about patients like stroke and cardiac in monitoring their expressions for better management of their diseases. Due to some privacy concerns, depth camera is a good candidate in such domains over RGB camera for facial expression recognition (FER). The accuracy of such FER systems are completely reliant on the extraction of the informative features. In this work, we have tested and validated the accuracy of a new feature extraction method based on symlet wavelet transform. In this method, the human face is divided into number of regions and in each region the movement of pixels have been traced in order to create the feature vectors. Each expression frame is decomposed up to 4 levels. In each decomposition level, the distance between the two corresponding pixels is found by using the distance formula in order to extract the most informative coefficients. After feature vector creation, Linear Discriminant Analysis (LDA) has been employed to reduce the dimensions of the feature space. Lastly, Hidden Markov Model (HMM) has been exploited for expression recognition. Most of the previous FER systems used existing available standard datasets and all the datasets were pose-based datasets. Therefore, we have collected our own depth data of 15 subjects by employing the dept camera. For the whole experiments, 10-fold cross validation scheme was utilized for the experiments. The proposed technique showed a significant improvement in accuracy against the existing works.

Item Details

Item Type:Refereed Conference Paper
Keywords:feature extraction
Research Division:Information and Computing Sciences
Research Group:Information systems
Research Field:Information systems not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Information services not elsewhere classified
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:98417
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2015-02-13
Last Modified:2018-01-16

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