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AuthorTran D.T.
AuthorKiranyaz S.
AuthorGabbouj M.
AuthorIosifidis A.
Available date2020-04-23T14:21:33Z
Publication Date2019
Publication NameProceedings - International Conference on Image Processing, ICIP
ResourceScopus
ISSN15224880
URIhttp://dx.doi.org/10.1109/ICIP.2019.8804296
URIhttp://hdl.handle.net/10576/14354
AbstractFace verification is a prominent biometric technique for identity authentication that has been used extensively in several security applications. In practice, face verification is often performed along with other visual surveillance tasks in the computing device. Thus, the ability to share the computation and reuse the information already extracted for other analysis tasks can greatly help reduce the computation load on the devices. In this study, we propose to utilize the knowledge transfer approach for the face verification problem by building a heterogeneous neural network architecture of Generalized Operational Perceptrons on top of the intermediate features extracted for object recognition purpose. Experimental results show that using our proposed approach, a face verification system can be incorporated into an existing visual analysis system with less additional memory and computational cost, compared to other similar approaches. - 2019 IEEE.
Languageen
PublisherIEEE Computer Society
SubjectFace Verification
Generalized Operational Perceptron
Progressive Neural Network Learning
TitleKnowledge Transfer for Face Verification Using Heterogeneous Generalized Operational Perceptrons
TypeConference Paper
Pagination1168-1172
Volume Number2019-September
dc.accessType Abstract Only


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