Investigation of nonlinear feature extraction techniques for facial emotion recognition

Hasimah Ali

Publication:

Investigation of nonlinear feature extraction techniques for facial emotion recognition

cris.virtual.department	Universiti Malaysia Perlis
cris.virtualsource.department	a768f986-3809-45bc-942a-8f8d1d378a0e
dc.contributor.author	Hasimah Ali
dc.date.accessioned	2024-12-06T08:38:49Z
dc.date.available	2024-12-06T08:38:49Z
dc.date.issued	2016
dc.description	Doctor of Philosophy in Mechatronic Engineering
dc.description.abstract	Over the last decades, facial emotion recognition has received a significant interest among researchers in areas of computer vision, pattern recognition and its related field. The increasing applications of facial emotion recognition have shown a sizeable impact in many areas ranging from psychology to human-computer interaction (HCI). Although facial emotion recognition has achieved a certain level of success, however its performance is far from human perception. Many approaches have been constantly proposed in the literature. In fact, the ability of facial emotion recognition to operate in fully automated with high accuracy remains challenging due to various problems such as intra-class variations, inter-class similarities and subtle changes of facial features. The adhered problem is further hampered as physiognomies of faces with respect to age, ethnicity and gender, thus increase the difficulties of recognizing the facial emotion. In order to resolve this problem, this thesis aims to develop nonlinear features extraction techniques of using Higher Order Spectra (HOS) and Empirical Mode Decomposition (EMD) separately in recognizing the seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise) from static images. A pre-processing step of isolating face region from different background was first employed by means of face detection. The 2-D facial image was then projected into 1-D facial signal by successive projection via Radon transform. Radon transform is translation and rotation invariant, hence preserves the variations in pixel intensities. The facial signal that describes the expression was extracted using HOS and EMD to obtain a set of significant features. In HOS framework, the third order statistic or bispectrum that captures contour (shape) and texture information was applied on facial signal. In this work, a new set of bispectral features was used to characterize the distinctive features of seven classes of emotion. While, in EMD framework, the facial signal was decomposed using EMD to produce a small set of intrinsic mode functions (IMFs) via sifting process. The IMF features which exhibit the unique pattern were used to differentiate the facial emotions.
dc.identifier.uri	https://hdl.handle.net/20.500.14170/10004
dc.language.iso	en
dc.subject	Facial expression
dc.subject	Emotion recognition
dc.subject	Pattern recognition systems
dc.subject	Human-computer interaction
dc.title	Investigation of nonlinear feature extraction techniques for facial emotion recognition
dc.type	Resource Types::text::thesis::doctoral thesis
dspace.entity.type	Publication
oaire.citation.endPage	176
oaire.citation.startPage	1
oairecerif.author.affiliation	Universiti Malaysia Perlis