基于条件生成对抗网络的手语样本骨架缺失关节点修复
Journal of Computer-Aided Design & Computer Graphics（2023）
特种光纤与先进通信国际合作联合实验室
Abstract
计算机视觉技术由于受到遮挡、视角和光照等因素的影响,对手语样本骨架关节点的检测通常存在缺失,导致手语识别准确率降低.为此,提出基于条件生成对抗网络(CGAN)的手语样本骨架缺失关节点修复方法.首先,通过分析手语样本残缺骨架中关节点的缺失分布情况,构建缺失关节点分布概率模型;其次,对完整骨架引入分布概率模型生成的缺失关节点,将这些残缺骨架用于CGAN框架中生成器和判别器的训练,通过CGAN框架训练好的生成器能够以残缺骨架为条件生成没有缺失的骨架;最后,用生成骨架去补全残缺骨架,即完成了修复.在中国手语数据集CSL上开展实验,生成器迭代训练80次后,生成骨架与完整骨架的平均均方根误差从0.019减小到0.001;在修复骨架缺失关节点的手语样本上,搭建手语识别网络迭代训练120次,与未进行修复相比,其识别准确率从90.6％提升为99.6％.实验结果表明,该方法能够有效地修复缺失关节点,极大地提升手语识别准确率.
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Key words
sign language samples,missing joint points,conditional generation adversarial networks,distribution probability model,sign language recognition
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