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

George Azzopardi, Frans Simanjuntak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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.
Original languageEnglish
Title of host publicationSpringer series "Advances in Intelligent Systems and Computing"
Publication statusPublished - 2018
EventComputer Vision Conference - Las Vegas, United States
Duration: 25-Apr-201926-Apr-2019
Conference number: 1


ConferenceComputer Vision Conference
Abbreviated titleCVC
Country/TerritoryUnited States
CityLas Vegas
Internet address

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