Face images have extremely useful information, including age, gender, identity, emotional information, etc. Face attribute is the specific description of the facial features, and face attribute analysis is to analyze the attributes related to the face according to the features displayed by the face. It has important application value in many fields, such as social security, digital entertainment intelligent monitoring, harmonious human-computer interaction, marketing analysis, finance, medical care, and smart home. Compared with traditional machine learning approaches, face attribute analysis based on deep learning technology has achieved outstanding results. However, face attribute analysis under unrestricted conditions is still challenging because of some unfavorable factors, such as scale, noise, illumination, occlusion, pose and subjective factors, etc. In order to solve these problems well, integration of more approaches is required, such as psychological, neuroscientific, and computational approaches to enhance cognition of face attributes.
Face attribute analysis is a very complex task. In the existing face attribute analysis algorithms, convolutional neural networks with deep learning are used to extract features. Important and effective features are extracted through the idea of the attention mechanism, which suppresses the weak and small features of the current task, which can improve the working efficiency of neural networks, and the attention mechanism is an important part of human cognitive function. In the process of discussing the issue of face attributes, the academic community needs to constantly innovate research approaches, and explore face attribute analysis methods and mechanisms from different angles. The goal of this research topic is to provide a platform for exchanging research works, technical trends, practical experience related to facial attribute analysis using Deep Neural Networks and intelligent vision technology, psychological, neuroscientific approaches.
The Editors solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal, especially encourage papers that integrate more approaches to achieve a more complete understanding. Topics of interest include (but are not limited to):
(1) Face recognition
(2) Facial age estimation and synthesis
(3) Facial expression recognition
(4) Facial attribute synthesis
(5) Psychological approaches for facial attribute analysis
(6) Cognition neuroscientific approaches for facial attribute analysis
(7) Lightweight model for intelligent vision technology in facial attribute analysis
Face images have extremely useful information, including age, gender, identity, emotional information, etc. Face attribute is the specific description of the facial features, and face attribute analysis is to analyze the attributes related to the face according to the features displayed by the face. It has important application value in many fields, such as social security, digital entertainment intelligent monitoring, harmonious human-computer interaction, marketing analysis, finance, medical care, and smart home. Compared with traditional machine learning approaches, face attribute analysis based on deep learning technology has achieved outstanding results. However, face attribute analysis under unrestricted conditions is still challenging because of some unfavorable factors, such as scale, noise, illumination, occlusion, pose and subjective factors, etc. In order to solve these problems well, integration of more approaches is required, such as psychological, neuroscientific, and computational approaches to enhance cognition of face attributes.
Face attribute analysis is a very complex task. In the existing face attribute analysis algorithms, convolutional neural networks with deep learning are used to extract features. Important and effective features are extracted through the idea of the attention mechanism, which suppresses the weak and small features of the current task, which can improve the working efficiency of neural networks, and the attention mechanism is an important part of human cognitive function. In the process of discussing the issue of face attributes, the academic community needs to constantly innovate research approaches, and explore face attribute analysis methods and mechanisms from different angles. The goal of this research topic is to provide a platform for exchanging research works, technical trends, practical experience related to facial attribute analysis using Deep Neural Networks and intelligent vision technology, psychological, neuroscientific approaches.
The Editors solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal, especially encourage papers that integrate more approaches to achieve a more complete understanding. Topics of interest include (but are not limited to):
(1) Face recognition
(2) Facial age estimation and synthesis
(3) Facial expression recognition
(4) Facial attribute synthesis
(5) Psychological approaches for facial attribute analysis
(6) Cognition neuroscientific approaches for facial attribute analysis
(7) Lightweight model for intelligent vision technology in facial attribute analysis