An end-to-end face parsing model using channel and spatial attentions

Hyungjoon Kim, Hyeonwoo Kim, Seongkuk Cho, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

Facial image parsing requires accurate extraction of facial components and features, and image segmentation can be used. Recently, various attention mechanisms showed excellent performance in segmentation by extracting features based on spatial and channel relationships for input images. In this paper, we propose a new face parsing technique using an attention block that combines the spatial attention block and the channel attention block to effectively utilize their functions. In this process, we improve the structure of the two blocks to compensate for their weaknesses. The attention block extracts features related to the shape of facial components from spatial relationships and concentrates on more important channels from correlation among channels. We built several segmentation models using the proposed block and compared their performance with well-known segmentation models. Experimental results showed that our combined block-based model can improve the segmentation accuracy by more than 5% in F1 score compared to other models.

Original languageEnglish
Article number110807
JournalMeasurement: Journal of the International Measurement Confederation
Volume191
DOIs
Publication statusPublished - 2022 Mar 15

Keywords

  • Attention mechanism
  • Face parsing
  • Image segmentation

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An end-to-end face parsing model using channel and spatial attentions'. Together they form a unique fingerprint.

Cite this