Face recognition using hybrid classifier systems
- 23 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1017-1022 vol.2
- https://doi.org/10.1109/icnn.1996.549037
Abstract
This paper considers hybrid classification architectures and shows their feasibility on large databases consisting of facial images. Our architecture, consists of an ensemble of connectionist networks-radial basis functions (RBF)-and decision trees (DT). This architecture enjoys robustness via (i) consensus provided by ensembles of RBF networks, and (ii) categorical classification using decision trees. The results reported in this paper on automatic face recognition using the FERET database are encouraging when one considers that the size of our test bed is in excess of 350 subjects and the great variability of the database. In addition we have also demonstrated the feasibility of our approach on queries aimed at the retrieval of frames ('images') using contextual cues.Keywords
This publication has 6 references indexed in Scilit:
- Human and machine recognition of faces: a surveyProceedings of the IEEE, 1995
- Testing face recognition systemsImage and Vision Computing, 1994
- Democracy in neural nets: Voting schemes for classificationNeural Networks, 1994
- The Meta-Pi network: building distributed knowledge representations for robust multisource pattern recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Query by committeePublished by Association for Computing Machinery (ACM) ,1992
- Automatic recognition and analysis of human faces and facial expressions: a surveyPattern Recognition, 1992