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Title: TAA-GCN: A temporally aware adaptive graph convolutional network for age estimation.
This paper proposes a novel age estimation algorithm, the Temporally-Aware Adaptive Graph Convolutional Network (TAA-GCN). Using a new representation based on graphs, the TAA-GCN utilizes skeletal, posture, clothing, and facial information to enrich the feature set associated with various ages. Such a novel graph representation has several advantages: First, reduced sensitivity to facial expression and other appearance variances; Second, ro- bustness to partial occlusion and non-frontal-planar viewpoint, which is commonplace in real-world applications such as video surveillance. The TAA-GCN employs two novel com- ponents, (1) the Temporal Memory Module (TMM) to compute temporal dependencies in age; (2) Adaptive Graph Convolutional Layer (AGCL) to refine the graphs and accommo- date the variance in appearance. The TAA-GCN outperforms the state-of-the-art methods on four public benchmarks, UTKFace, MORPHII, CACD, and FG-NET. Moreover, the TAA-GCN showed reliability in di↵erent camera viewpoints and reduced quality images.  more » « less
Award ID(s):
2000487
PAR ID:
10448464
Author(s) / Creator(s):
; ;
Editor(s):
Hancock, E.
Date Published:
Journal Name:
Pattern recognition
Volume:
134
ISSN:
0031-3203
Page Range / eLocation ID:
1-36
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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