融合表示学习的中医面部穴位检测框架

A Facial Acupoint Detection Framework for Traditional Chinese Medicine by Incorporating Feature Representation Learning

现有智能穴位检测方法存在依赖红外等外部设备、特征表示挖掘不足、穴位检测精度较低等问题。在分析穴位检测需求的基础上,将其定义为基于视觉图像的关键点检测任务,提出融合特征表示学习的中医面部穴位检测模型框架FADbR。首先,构建基于自监督学习机制的对抗自编码网络模型,通过人脸图像重建任务实现特征表示学习,利用神经网络提取人脸隐性知识,深度挖掘面部抽象特征。随后,基于自监督学习对抗自编码器构建监督学习面部穴位检测模型,充分利用学习到的人脸隐性知识提高智能面部穴位检测精度。最后,基于现有人脸数据库构建稠密人脸穴位数据集FAcupoint并用于方法验证。实验结果表明,FADbR可以通过表示学习挖掘面部关键特征支撑穴位检测任务,即使在少量训练样本的情况下也能够获得较好的检测性能。

Existing acupoint detection(AD)approaches suffer from extra-equipment-dependent,shallow feature representation,and poor accuracy issues.In this work,the AD task is defined as the key-point detection based on visual images by analyzing the task nature.A novel paradigm called facial acupoint detection by reconstruction(FADbR)is designed to achieve the facial AD task.Firstly,the adversarial autoencoder architecture serves as the backbone network based on the self-supervised learning mechanism.The image-to-image reconstruction procedure is performed to enhance the feature representation ability,in which the neural architecture is applied to capture hidden representations and abstract knowledge of the human face.In succession,the FADbR framework is constructed to implement the AD task in a supervised manner by designing the interleaved layers to output the heatmap for each acupoint.Because of the reconstruction procedure,a fine-grained model can be achieved to improve AD performance by the learned facial representations.A new dataset called FAcupoint is built to validate the proposed approach using a public human face dataset.Experimental results on the new dataset demonstrate that the proposed FADbR framework has the ability to extract high-level feature representation to improve AD performance.Most importantly,the FADbR framework can achieve preferred performance with small training samples,which further validates the reconstruction paradigm in this work.