Cross-dataset heterogeneous adaptation learning based facial attributes estimation

• Published in: Multimedia tools and applications Vol. 81; no. 25; pp. 36489 - 36504
• Main Authors: Tian, Qing, Chu, Yi, Zhang, Fengyuan, Wang, Chao, Liu, Mengyu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2022; Springer Nature B.V
• Subjects: 1213: Computational Optimization and Applications for Heterogeneous Multimedia Data; Adaptation; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Datasets; Image resolution; Labels; Learning; Multimedia Information Systems; Pattern recognition; Regularization; Special Purpose and Application-Based Systems; Heterogeneous adaptation; Joint estimation; Facial attributes estimation; Cross-dataset
• DOI: 10.1007/s11042-022-13544-1
• ISSN: 1380-7501

Recently, human facial attributes analysis has become an important research topic in the field of pattern recognition and computer vision. In fact, various tasks reveal related but different patterns between facial age attribute, race attribute, and gender attribute. Therefore, it is important to construct a facial multi-attribute estimation model to reveal the relationship between different attributes. However, on the one hand, there are some drawbacks in existing facial datasets, such as the lack of some attribute labels or incomplete attribute distribution, so it is infeasible to realize facial multi-attribute estimation on single facial dataset at the same time. On the other hand, in different datasets facial attributes features and labels tend to be heterogeneous, the distribution divergence and the dimension differences due to the changes in collection equipment and image resolution. To this end, this work first proposes the Cross-dataset heterogeneous Adaptation learning facial multiple attributeS joint Estimation (CASE) to mitigate distribution divergence among different facial attributes. Firstly, this work adopts different coding strategies for different face attributes, to maintain the inherent attributes of face attributes. Secondly, in order to explore the potential relationship between labels of different attributes, labels of different attributes are merged and the output relation regularization term for multi-label mapping projection is constructed. Finally, extensive experiments have testified the effectiveness and superiority of the proposed methods.