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Multimedia Systems
Erschienen in: Multimedia Systems 4/2023

25.04.2023 | Regular Paper

Face attribute recognition via end-to-end weakly supervised regional location

verfasst von: Jian Shi, Ge Sun, Jinyu Zhang, Zhihui Wang, Haojie Li

Erschienen in: Multimedia Systems | Ausgabe 4/2023

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Abstract

Facial attributes have been successfully applied in many fields of computer vision, such as face recognition, face retrieval, and face image synthesis. Locating attribute-related facial regions is a prerequisite for predicting the presence of attributes. However, most existing face attribute recognition methods obtain specific attribute regions through face segmentation annotations or face landmarks that are not easily available. In this paper, we propose a weakly supervised attribute location module (ALM) that can effectively detect facial regions with only image-level attribute labels and improve face attribute recognition using region-based local features. Moreover, we introduce a bottom-up skip connection structure to fuse the features from multiple convolutional layers, which can enhance attribute-specific region location with low-level spatial information supplements. Our network is easy to build and can be trained in an end-to-end manner. Extensive experiments demonstrate the superior performance of our method on LFWA and CelebA datasets.
Vorheriger Artikel Residual guided coordinate attention for selection channel aware image steganalysis
Nächster Artikel Weakly supervised anomaly detection with multi-level contextual modeling

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Metadaten
Titel
Face attribute recognition via end-to-end weakly supervised regional location
verfasst von
Jian Shi
Ge Sun
Jinyu Zhang
Zhihui Wang
Haojie Li
Publikationsdatum
25.04.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 4/2023
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01095-w

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