Standard DatasetsIndoor Facial 130 Expressions Dataset
The progress of video and photo editing applications relies heavily on high-quality datasets for machine learning models training. Our carefully selected datasets play a crucial role in enhancing the abilities of such applications, by offering accurate segmentation and recognition of different elements within images and videos.
The "Indoor Facial 130 Expressions Dataset" is designed for applications in media & entertainment and mobile sectors, featuring a collection of internet-collected indoor facial images with resolutions ranging from 443 x 443 to 1127 x 1080 pixels. This dataset specializes in key points annotation, providing 130 key points for each facial expression, offering a detailed foundation for emotion recognition, facial animation, and interactive applications.
Sample
Specification
Dataset ID
MD-Image-074
Dataset Name
Indoor Facial 130 Expressions Dataset
Data Type
ImageVolume
About 4k
Data Collection
Internet collected images. Resolution is from 443 x 443 to 1127 x 1080.
Annotation
Key Points
Annotation Notes
130key points.
Application Scenarios
Media & Entertainment;Mobile
Data Collections
This dataset comprises approximately 4k images, each annotated with 130 key points to capture a wide array of facial expressions. The extensive key points coverage allows for precise modeling of facial movements and expressions, making it invaluable for developing advanced facial recognition technologies, enhancing character animation in media and entertainment, and improving user interaction in mobile applications, by providing nuanced insights into a broad spectrum of human emotions and expressions.
Quality Assurance
Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
FASSEG Repository [Learn more]
This collection offers datasets for frontal face segmentation (Frontal01 and Frontal02) and a dataset for faces in multiple poses (Multipose01).These datasets can be valuable for training models to perform face segmentation in different orientations and conditions.
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