Authors:
Humayra Binte Ali
1
and
David M. W. Powers
2
Affiliations:
1
Flinders University, Australia
;
2
Flinders University and Beijing University of Technology, Australia
Keyword(s):
NMF-Non Negative Matrix Factorization, OEPA- Optimal Expression- specific Parts Accumulation, FR-Face Recognition, FER- Facial Expression Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Intelligent User Interfaces
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
;
Vision and Perception
Abstract:
Face and facial expression recognition is a broad research domain in machine learning domain. Non-negative
matrix factorization (NMF) is a very recent technique for data decomposition and image analysis. Here we
propose face identification system as well as a facial expression recognition, which is a system based on NMF.
We get a significant result for face recognition. We test on CK+ and JAFFE dataset and we find the face
identification accuracy is nearly 99% and 96.5% respectively. But the facial expression recognition (FER) rate
is not as good as it required for the real life implementation. To increase the detection rate for facial expression
recognition, our propose fusion based NMF, named as OEPA-NMF, where OEPA means Optimal Expression specific
Parts Accumulation. Our experimental result shows OEPA-NMF outperforms the prevalence NMF
for facial expression recognition. As face identification using NMF has a good accuracy rate, so we are not
interested to apply OEPA-NMF
for face identification.
(More)