Authors:
Simon Nash
;
Mark Rhodes
and
Joanna Isabelle Olszewska
Affiliation:
University of Gloucestershire, United Kingdom
Keyword(s):
Face Detection, Face Recognition, AdaBoost, Template, Facial Features, Eigenfaces, Pose Correction, Human-Computer Interactive Systems.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Cybernetics and User Interface Technologies
;
Data Manipulation
;
Detection and Identification
;
Devices
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Information and Systems Security
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Systems
;
Sensor Networks
;
Soft Computing
;
Telecommunications
Abstract:
Although face recognition applications are growing, robust face recognition is still a challenging task due e.g. to variations in face poses, facial expressions, or lighting conditions. In this paper, we propose a new method which allows both automatic face detection and recognition and incorporates an interactive selection of facial features in conjunction with our new pose-correction algorithm. Our resulting system we called iFR successfully recognizes faces across pose, while being computationally efficient and outperforming standard approaches, as demonstrated in tests carried out on publicly available standard datasets.