40 People – 3D&2D Living_Face & Anti

21,300 Images - Human Body Segmentation Data

21,300 Images - Human Body Segmentation Data. The data includes indoor scenes and outdoor scenes. The data covers female people and male people. The race distribution includes Asian, black race and Caucasian. The age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The dataset diversity includes multiple scenes, ages, races, postures, and appendages. In terms of annotation, we adpoted pixel-wise segmentation annotations on human body. The data can be used for tasks such as human body segmentation.

Multiple scenes Multiple ages Multiple races Multiple postures Multiple appendages

5,993 People – Infrared Face Recognition Data

5,993 People – Infrared Face Recognition Data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is realsense D453i. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition.

infrared face recognition indoor scenes outdoor scenes realsense D453i multiple age periods multiple facial postures multiple scenes

34,981 Images – Alpha Matte Human Body Segmentation Data (Fine Version)

34,981 Images – Alpha Matte Human Body Segmentation Data. The data includes indoor scenes and outdoor scenes. The dataset diversity includes multiple scenes, multiple age groups, multiple human body angles, multiple postures. In terms of annotation, alpha matte segmentation annotation was adopted for the human body. The data can be used for tasks such as alpha matte human body segmentation.

Alpha matte human body segmentation multiple scenes multiple age groups multiple human body angles multiple postures

4,458 People - 3D Facial Expressions Recognition Data

4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.

3D facial expressions recognition different expressions different ages different races different collecting scenes

5,199 People – 3D Face Recognition Images Data

5,199 People – 3D Face Recognition Images Data. The collection scene is indoor scene. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes multiple facial postures, multiple light conditions, multiple indoor scenes. This data can be used for tasks such as 3D face recognition.

3D Face Recognition multiple facial postures multiple light conditions multiple indoor scenes

1,417 People – 3D Living_Face & Anti_Spoofing Data

1,417 People – 3D Living_Face & Anti_Spoofing Data. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.

3D Living_Face & Anti_Spoofing various expressions facial postures anti-spoofing samples multiple light conditions multiple scenes

11,113 People - Face Recognition Data with Gauze Mask

11,113 People - Face Recognition Data with Gauze Mask, for each subject, 7 images were collected. The dataset diversity includes multiple mask types, multiple ages, multiple races, multiple light conditions and scenes.This data can be applied to computer vision tasks such as occluded face detection and recognition.

Face recognition Face occlusion Frontal face Gause mask

2,937 People with Occlusion and Multi-pose Face Recognition Data

2,937 People with Occlusion and Multi-pose Face Recognition Data, for each subject, 200 images were collected. The 200 images includes 4 kinds of light conditions * 10 kinds of occlusion cases (including non-occluded case) * 5 kinds of face pose. This data can be applied to computer vision tasks such as occluded face detection and recognition.

Face recognition Face occlusion Multi-pose per person Face with mask mask-on face detection

40 People – 3D&2D Living_Face & Anti_Spoofing Data

2D face recognition

3D face recognition

anti-spoofing

iPhone of multiple models

indoor scenes

outdoor scenes

multiple devices

multiple actions

multiple facial postures

multiple anti-spoofing

40 People – 3D&2D Living_Face & Anti_Spoofing Data

2D face recognition 3D face recognition anti-spoofing iPhone of multiple models indoor scenes outdoor scenes multiple devices multiple actions multiple facial postures multiple anti-spoofing

2D face recognition

3D face recognition

anti-spoofing

iPhone of multiple models

indoor scenes

outdoor scenes

multiple devices

multiple actions

multiple facial postures

multiple anti-spoofing