Expression Recognition with Deep Features Extracted from Holistic and Part-based Models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Image and Vision Computing Year : 2020

Expression Recognition with Deep Features Extracted from Holistic and Part-based Models

Abstract

Facial expression recognition aims to accurately interpret facial muscle movements in affective states (emotions). Previous studies have proposed holistic analysis of the face, as well as the extraction of features pertained only to specific facial regions towards expression recognition. While classically the latter have shown better performances, we here explore this in the context of deep learning. In particular, this work provides a performance comparison of holistic and part-based deep learning models for expression recognition. In addition, we showcase the effectiveness of skip connections, which allow a network to infer from both low and high-level feature maps. Our results suggest that holistic models outperform part-based models, in the absence of skip connections. Finally, based on our findings, we propose a data augmentation scheme, which we incorporate in a part-based model. The proposed multi-face multi-part (MFMP) model leverages the wide information from part-based data augmentation, where we train the network using the facial parts extracted from different face samples of the same expression class. Extensive experiments on publicly available datasets show a significant improvement of facial expression classification with the proposed MFMP framework.
Fichier principal
Vignette du fichier
IVC___Deep_features_from_Holistic_and_Part_based_Models.pdf (2.71 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02972172 , version 1 (20-10-2020)

Identifiers

  • HAL Id : hal-02972172 , version 1

Cite

S L Happy, Antitza Dantcheva, Francois F Bremond. Expression Recognition with Deep Features Extracted from Holistic and Part-based Models. Image and Vision Computing, 2020. ⟨hal-02972172⟩
39 View
304 Download

Share

Gmail Facebook X LinkedIn More