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Knowledge Transfer for Face Verification Using Heterogeneous Generalized Operational Perceptrons

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE International Conference on Image Processing (ICIP)
KustantajaIEEE
Sivut1168-1172
Sivumäärä5
ISBN (elektroninen)978-1-5386-6249-6
ISBN (painettu)978-1-5386-6250-2
DOI - pysyväislinkit
TilaJulkaistu - syyskuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiIEEE International Conference on Image Processing
ISSN (painettu)1522-4880
ISSN (elektroninen)2381-8549

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
Ajanjakso1/01/00 → …

Tiivistelmä

Face 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.

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