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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

Multi-label learning of face demographic classification for correlation analysis

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Journal of Shanghai University(English Edition) 2011年第5期15卷 352-356页

作者：方昱春程功罗婕 School of Computer Engineering and Science Shanghai University

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.

关键词： denlographic classification multi-label learning face analysis

Study on Real-Time Heart Rate Detection Based on Multi-People

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Computer Systems Science & Engineering 2023年第2期44卷 1397-1408页

作者： Qiuyu Hu Wu Zeng Yi Sheng Jian Xu Weihua Ou Ruochen Tan School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430000China School of Electronic and Information Engineering Guizhou Normal UniversityGuizhou550000China Interactive Media and Computer Science University of MiamiMiami33124FloridaUSA

Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Therefore,the study of non-contact heart rate measurement methods is of great importance.Based on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking algorithm.Then after the regional segmentation of the facial image,the signal acquisition of the region of interest is further resolved.Finally,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletﬁlter.The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.

关键词： face recognition face analysis heart rate detection IPPG signal

Race Classification Using Deep Learning

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Computers, Materials & Continua 2021年第9期68卷 3483-3498页

作者： Khalil Khan Rehan Ullah Khan Jehad Ali Irfan Uddin Sahib Khan Byeong-hee Roh Department of Information Technology and Computer Science Pak-Austria Institute of Applied Science and TechnologyHaripurPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwonKorea Department of Computer Science Superior CollegeLahorePakistan Department of Electronics and Telecommunications Politecnico di TorinoTorinoItaly

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.

关键词： Deep learning facial feature face analysis learning race race classification