CBR: fuzzified case retrieval approach for facial expression recognition

Authors:
Assia Khanum;M. Zubair Shafiq;Ejaz Muhammad
Affiliations:
College of Electrical and Mechanical Engineering, National University of Science and Technology, Rawalpindi, Pakistan;College of Electrical and Mechanical Engineering, National University of Science and Technology, Rawalpindi, Pakistan;College of Electrical and Mechanical Engineering, National University of Science and Technology, Rawalpindi, Pakistan
Venue:
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Year:
2007

Citing 4
Cited 2

Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
Fuzzy sets and fuzzy logic: theory and applications

Fuzzy sets and fuzzy logic: theory and applications
Analysis, interpretation and synthesis of facial expressions

Analysis, interpretation and synthesis of facial expressions
Face to interface: facial affect in (hu)man and machine

Proceedings of the SIGCHI conference on Human Factors in Computing Systems

Fuzzy case-based reasoning for facial expression recognition

Fuzzy Sets and Systems
Knowledge and intelligent computing system in medicine

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new scheme for the recognition of facial expressions from a set of facial features using fuzzy enhanced Case-based Reasoning (CBR). Facial expression recognition has become the cornerstone of human-computer interaction (HCI) systems. It has wide range applications information systems and e-commerce such as intelligent desktops and intelligent web agents. Our system is the integration of two fundamental paradigms of AI, i.e., Fuzzy logic and Case Based Reasoning. Fuzzy logic is embedded into our CBR system for improved case retrieval. A dual, fuzzy similarity determination module is the core of our system and works within the Case-based Reasoning system. The advantages from Fuzzy Rule Based Systems (FRBS) like linguistic modeling and fault tolerance when combined with the reasoning capability of CBR greatly improved the sophistication and utility of our system. A reinforcing combination of two approaches might lead to exciting applications that are yet to be envisaged.