TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation
TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation
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DOI：
10.1016/j.patcog.2022.109066
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Matthew Korban;Peter Youngs;S. Acton
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文献类型：
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作者：
Matthew Korban;Peter Youngs;S. Acton
本文提出了一种新的年龄估计算法，时间感知自适应图卷积网络（TAA-GCN）。使用基于图形的新表示，TAA-GCN利用骨骼，姿势，服装和面部信息来丰富与各种年龄相关的特征集。这种新颖的图形表示具有以下几个优点：第一，降低了对面部表情和其他外观变化的敏感性;第二，对部分遮挡和非正面平面视点的鲁棒性，这在视频监控等现实应用中很常见。TAA-GCN采用了两个新的组件，（1）时间记忆模块（TMM），以计算时间依赖性的年龄;（2）自适应图卷积层（AGCL），以细化图形和适应外观的变化。TAA-GCN在UTKFace、MORPHII、CACD和FG-NET四个公共基准测试中的表现优于最先进的方法。此外，TAA-GCN在不同的相机视角和降低质量的图像中显示出可靠性。
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, robustness 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 components, (1) the Temporal Memory Module (TMM) to compute temporal dependencies in age; (2) Adaptive Graph Convolutional Layer (AGCL) to refine the graphs and accommodate 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 different camera viewpoints and reduced quality images.