Masked Neural Sparse Encoder for Face Occlusion Detection

Bing Fei Wu, Yi Chiao Wu

研究成果: Conference contribution同行評審

摘要

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%.

原文English
主出版物標題2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2471-2476
頁數6
2020-October
ISBN(電子)9781728185262
DOIs
出版狀態Published - 11 10月 2020
事件2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
持續時間: 11 10月 202014 10月 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
國家/地區Canada
城市Toronto
期間11/10/2014/10/20

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