Fusion of CNN- and COSFIRE-based features with application to Gender Recognition from Face Images

George Azzopardi, Frans Simanjuntak

OnderzoeksoutputAcademicpeer review

5 Citaten (Scopus)
7 Downloads (Pure)


Convolution neural networks (CNNs) have been demonstrated to be very eective in various computer vision tasks. The main strength of such networks is that features are learned from some training data. In cases where training data is not abundant, transfer learning can be used in order to adapt features that are pre-trained from other tasks. Similarly, the COSFIRE approach is also trainable as it configures lters to be selective for features selected from training data. In this study we propose a fusion method of these two approaches and evaluate their performance on the application of gender recognition from face images. In particular, we use the pre-trained VGGFace CNN, which when used as standalone, it achieved 97.45% on the GENDER-FERET data set. With one of the proposed fusion approaches the recognition rate on the same task is improved to 98.9%, that is reducing the error rate by more than 50%. Our experiments demonstrate that COSFIRE filters can provide complementary features to CNNs, which contribute to a better performance.
Originele taal-2English
TitelSpringer series "Advances in Intelligent Systems and Computing"
Aantal pagina's15
StatusPublished - 24-apr.-2019
EvenementComputer Vision Conference - Las Vegas, United States
Duur: 25-apr.-201926-apr.-2019
Congresnummer: 1


ConferenceComputer Vision Conference
Verkorte titelCVC
Land/RegioUnited States
StadLas Vegas
Internet adres

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