Selective Transfer Machine for Personalized Facial Expression Analysis
نویسندگان
چکیده
منابع مشابه
Facial Expression Analysis by Machine Learning
Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regul...
متن کاملFacial Action Transfer with Personalized Bilinear Regression
Facial Action Transfer (FAT) has recently attracted much attention in computer vision due to its diverse applications in the movie industry, computer games, and privacy protection. The goal of FAT is to “clone” the facial actions from the videos of one person (source) to another person (target). In this paper, we will assume that we have a video of the source person but only one frontal image o...
متن کاملFacial Expression Recognition based on Personalized
An online facial expression recognition system based on personalized galleries is presented. This system is built on the framework of the PersonSpotter system, which is able to track and detect the face of a person in a live video sequence. By utilizing the recognition method of Elastic Graph Matching, the most similar person whose images are stored in the gallery can be found, then the persona...
متن کاملExpression transfer for facial sketch animation
This paper presents a hierarchical animation method for transferring facial expressions extracted from a performance video to different facial sketches. Without any expression example obtained from target faces, our approach can transfer expressions by motion retargetting to facial sketches. However, in practical applications, the image noise in each frame will reduce the feature extraction acc...
متن کاملFacial expression and selective attention
Recent findings demonstrate that faces with an emotional expression tend to attract attention more than neutral faces, especially when having some threat-related value (anger or fear). These findings suggest that discrimination of emotional cues in faces can at least partly be extracted at preattentive or unconscious stages of processing, and then serve to enhance awareness and behavioural resp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2017
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2016.2547397