Face recognition using hybrid systems

Srinivas Gutta; Jeffrey R.-J. Huang; Harry Wechsler; Barnabas Takacs

doi:10.1117/12.267832

26 February 1997 Face recognition using hybrid systems

Srinivas Gutta, Jeffrey R.-J. Huang, Harry Wechsler, Barnabas Takacs

Proceedings Volume 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision; (1997) https://doi.org/10.1117/12.267832
Event: 25th Annual AIPR Workshop on Emerging Applications of Computer Vision, 1996, Washington, DC, United States

Abstract

We describe a novel approach for fully automated face recognition and show its feasibility on a large data base of facial images (FERET). Our approach, based on a hybrid architecture consisting of an ensemble of connectionist networks -- radial basis functions (RBF) -- and inductive decision trees (DT), combines the merits of 'discrete and abstractive' features with those of 'holistic' template matching.' Training for face detection takes place over both positive and negative examples. The benefits of our architecture include (1) robust detection of facial landmarks using decision trees, and (2) robust face recognition using consensus methods over ensembles of RBF networks. Experiments carried out using k-fold cross validation on a large data base consisting of 748 images corresponding to 374 subjects, among them 11 duplicates, yield on the average 87% correct match, and (ROC curves where) 99% correct verification is achieved for a 2% reject rate.

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Srinivas Gutta, Jeffrey R.-J. Huang, Harry Wechsler, and Barnabas Takacs "Face recognition using hybrid systems", Proc. SPIE 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision, (26 February 1997); https://doi.org/10.1117/12.267832

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KEYWORDS

Facial recognition systems

Eye

Image processing

Surveillance

Databases

Network architectures

Neural networks

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