Computer Vision Datasets

This dataset contains 87,871 face images with annotations for 106 facial landmarks, includes yellow, black, white and Indian races. In order to be more challenging, the data includes multiple scenes, multiple poses, different ages, light conditions and complicated expressions. This data can be used for tasks such as face detection models, face recognition systems, and facial landmark detection algorithms in AI and computer vision.

This dataset contains facial expression recognition data from 1,142 people in online conference scenes. Participants include Asian, Caucasian, Black, and Brown individuals, mainly young and middle-aged adults. Data was collected across a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries , bedroom, etc., Each participant performed seven key expressions: normal, happy, surprised, sad, angry, disgusted, and fearful. The dataset is suitable for tasks such as facial expression recognition, emotion recognition, human-computer interaction, and video conferencing AI applications.

This dataset provides 26,090 facial images.The data includes the following five types of facial skin defects: acne, acne marks, stains, wrinkles, and dark circles. This data can be used for tasks such as skin defects detection and skincare AI.

This dataset contains 7,262 facial images with 2,499,771 annotated boxes. The data includes the following seven types of facial skin defects: acne, moles, scars, herpes (sores), speckles, freckles, and others. This data can be used for tasks such as skin defects detection, dermatology AI, and medical image analysis.

This dataset contains 25,581 face images. The dataset includes Asian, black, Caucasian and brown races. In order to be more challenging, the data includes multiple scenes, multiple poses, different ages, light conditions and complicated expressions. For annotation, 88 facial landmarks and visible and invisible attributes of landmarks were annotated. This data can be used for tasks such as face detection and recognition models, facial landmark detection algorithms.

The Cat and Dog Facial Landmarks Dataset contains 80,000 annotated images across a wide range of indoor and outdoor environments. The data diversity includes multiple scenes, multiple dog and cat breeds, different face angles, different shooting distances. Each image is annotated with facial landmarks (keypoints). This dataset can be used for tasks such as cat and dog face recognition, breed classification, pet identity authentication, biometric verification, and animal face detection

This dataset contains facial expression recognition data from 1,323 drivers, recorded under different ages, time periods, and expression variations. In terms of acquisition equipment, RGB and infrared binocular cameras are used. This dataset of driver expression recognition data can be used for driver monitoring systems (DMS), driver fatigue and drowsiness detection, In-cabin behavior analysis and other tasks.

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.

The 399 Asians - 35,112 Images Multi-pose Face Data with 21 Facial Landmarks Annotation data is collected from 399 people. The data diversity includes multiple poses, different ages, different light conditions and multiple scenes. This data can be used for tasks such as face detection and face recognition. Thee accuracy of labels of gender, face pose, year of birth, light condition, scene and wearing glasses or not is more than 97%;annotation accuracy of facial landmarks is more than 97%