English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Facial Expression Recognition from static images is a challenging problem incomputer vision applications. Convolutional Neural Network (CNN), thestate-of-the-art method for various computer vision tasks, has had limitedsuccess in predicting expressions from faces having extreme poses,illumination, and occlusion conditions. To mitigate this issue, CNNs are oftenaccompanied by techniques like transfer, multi-task, or ensemble learning thatoften provide high accuracy at the cost of increased computational complexity.In this work, we propose a Part-based Ensemble Transfer Learning network thatmodels how humans recognize facial expressions by correlating the spatialorientation pattern of the facial features with a specific expression. Itconsists of 5 sub-networks, and each sub-network performs transfer learningfrom one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth,or jaw to expression classification. We show that our proposed ensemble networkuses visual patterns emanating from facial muscles' motor movements to predictexpressions and demonstrate the usefulness of transfer learning from FacialLandmark Localization to Facial Expression Recognition. We test the proposednetwork on the CK+, JAFFE, and SFEW datasets, and it outperforms the benchmarkfor CK+ and JAFFE datasets by 0.51% and 5.34%, respectively. Additionally, theproposed ensemble network consists of only 1.65M model parameters, ensuringcomputational efficiency during training and real-time deployment. Grad-CAMvisualizations of our proposed ensemble highlight the complementary nature ofits sub-networks, a key design parameter of an effective ensemble network.Lastly, cross-dataset evaluation results reveal that our proposed ensemble hasa high generalization capacity, making it suitable for real-world usage.

Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images