Masked Neural Sparse Encoder for Face Occlusion Detection

Bing Fei Wu, Yi Chiao Wu

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

Abstract

This paper presents an effective way to extract low-level features based on sparse coding for facial occlusion detection. Masked Neural Sparse Encoder (MNSE) is proposed to be a sparse coding solver that brings out better feature bases for data representation and improvement in the anomaly detection task. To guarantee the representational capability of features, a set of masks is applied to force each feature basis is heeded on learning a specific stroke within a certain area. The mask set is constructed by clustering primary strokes from training samples, and represents them with corresponding centers of clusters. Hence, these masks stand for main strokes in concerned areas with higher probabilities. Experiments show MNSE contains better representational capability in data from different domains. Compared with the standard sparse coding and the auto-encoder based approaches, MNSE lifts the accuracy up around 20%.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2471-2476
Number of pages6
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
StatePublished - 11 Oct 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: 11 Oct 202014 Oct 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
CountryCanada
CityToronto
Period11/10/2014/10/20

Keywords

  • Anomaly Detection
  • Auto-encoder
  • Sparse Coding

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