Classification research of TCM pulse conditions based on multi-label voice analysis

Objective To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Multi-label learning of face demographic classification for correlation analysis

In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks.

Race Classification Using Deep Learning

Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.

Multi-label dimensionality reduction based on semi-supervised discriminant analysis

Multi-label data with high dimensionality often occurs,which will produce large time and energy overheads when directly used in classification tasks.To solve this problem,a novel algorithm called multi-label dimensionality reduction via semi-supervised discriminant analysis(MSDA) was proposed.It was expected to derive an objective discriminant function as smooth as possible on the data manifold by multi-label learning and semi-supervised learning.By virtue of the latent imformation,which was provided by the graph weighted matrix of sample attributes and the similarity correlation matrix of partial sample labels,MSDA readily made the separability between different classes achieve maximization and estimated the intrinsic geometric structure in the lower manifold space by employing unlabeled data.Extensive experimental results on several real multi-label datasets show that after dimensionality reduction using MSDA,the average classification accuracy is about 9.71% higher than that of other algorithms,and several evaluation metrices like Hamming-loss are also superior to those of other dimensionality reduction methods.

面向人脸表情分析的人脸图像正脸化方法

在人脸表情分析过程中,头部姿态变化常会引起人脸信息的不对称,传统上仅对人脸图像进行裁剪和对齐的相关操作难以得到对姿态鲁棒的特征。为获取人脸结构化的特征,提出了一种人脸图像正脸化处理方法。该方法将检测到的人脸关键点映射到新的二维空间进行关键点的正脸化,将正脸化后的关键点还原到原始图像中作为新关键点,通过移动最小二乘法指导图像由原始关键点向新关键点变形,得到正脸化后的人脸图像。在公共的RAF-DB和ExpW人脸表情数据集上,采用上述处理方法对人脸图像进行预处理,并在VGG16和ResNet50深度学习网络中进行人脸表情分类任务的模型训练,用分类任务的准确率来评估文中正脸化方法对人脸表情分析的有效性。实验结果表明,该方法在人脸表情分析方面优于深度学习中传统的预处理方法,并且可以有效提高人脸的信息质量。

机器仿生眼的多任务学习人脸分析

智能机器人中人机交互的性能至关重要,人脸分析可以使人机交互变得更友善.文中提出可以同时进行笑容识别和性别分类的多任务学习卷积神经网络,同时学习存在内在相关性的任务,提升单个任务的性能.在Celeb A数据集的测试集上,文中网络在笑容识别任务和性别分类任务中均获取较高准确率.在设计的机器仿生眼上验证文中模型,获得良好的笑容识别效果和性别分类效果.文中对人脸分析进行的研究可以提升与机器仿生眼人机交互的能力.

基于相关性判别分析的人脸图像分类算法

针对目前的判别分类方法不能有效分析数据之间相关性的问题,提出新的判别分类算法,并将其应用于人脸图像识别。首先,将传统的相关性分析模型拓展成有监督的形式,分别设计提取类内和类间相关性特征的目标函数,寻找投影变换以最大化类内相关性特征并且最小化类间相关性特征;进一步,对样本数据进行判别分析,使得投影之后同类样本之间散度最小化并且异类样本之间散度最大化;最后构建约束形式的相关性判别模型进行优化求解,并使用最近邻分类进行分类。实验结果表明,在AR人脸数据集上与对比算法相比能够将分类识别率提高1.01%~5.58%,在FERET人脸数据集上与对比算法相比能够将分类识别率提高1.87%~5.69%,实验结果与理论分析数据相符合,本算法能够有效地提高分类精度。