[{"@type":"PropertyValue","name":"Data size","value":"821 people"},{"@type":"PropertyValue","name":"Population distribution","value":"gender distribution: 374 males, 447 females; race distribution:Vietnam, Indonesia; age distribution: 18~45 years old, 46~60 years old, over 60 years old"},{"@type":"PropertyValue","name":"Collecting environment","value":"in-car Cameras"},{"@type":"PropertyValue","name":"Data diversity","value":"multiple expressions, multiple ages, multiple time periods"},{"@type":"PropertyValue","name":"Device","value":"visible light and infrared binocular camera, resolution 1,920x1,080"},{"@type":"PropertyValue","name":"Shooting position","value":"the center of the inside rear view mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position, rearview mirror wide angle lens position"},{"@type":"PropertyValue","name":"Collecting time","value":"day, evening, night"},{"@type":"PropertyValue","name":"Vehicle Type","value":"Car, SUV, MVP, Truck, Bus"},{"@type":"PropertyValue","name":"Data Format","value":"the video data format is .mp4"},{"@type":"PropertyValue","name":"Accuracy","value":"according to the accuracy of each person's acquisition expression, the accuracy exceeds 95%;the accuracy of label annotation is not less than 95%"}]
{"id":1293,"datatype":"1","titleimg":"https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp","type1":"147","type1str":null,"type2":"151","type2str":null,"dataname":"Driver Monitoring Dataset – 7 Facial Expressions from 821 Drivers","datazy":[{"title":"Data size","content":"821 people","desc":"Data size"},{"title":"Population distribution","content":"gender distribution: 374 males, 447 females; race distribution:Vietnam, Indonesia; age distribution: 18~45 years old, 46~60 years old, over 60 years old","desc":"Population distribution"},{"title":"Collecting environment","content":"in-car Cameras","desc":"Collecting environment"},{"title":"Data diversity","content":"multiple expressions, multiple ages, multiple time periods","desc":"Data diversity"},{"title":"Device","content":"visible light and infrared binocular camera, resolution 1,920x1,080","desc":"Device"},{"title":"Shooting position","content":"the center of the inside rear view mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position, rearview mirror wide angle lens position","desc":"Shooting position"},{"title":"Collecting time","content":"day, evening, night","desc":"Collecting time"},{"title":"Vehicle Type","content":"Car, SUV, MVP, Truck, Bus","desc":"Vehicle Type"},{"title":"Data Format","content":"the video data format is .mp4","desc":"Data Format"},{"title":"Accuracy","content":"according to the accuracy of each person's acquisition expression, the accuracy exceeds 95%;the accuracy of label annotation is not less than 95%","desc":"Accuracy"}],"datatag":"Multiple expressions,Multiple ages,Multiple time periods","technologydoc":null,"downurl":null,"datainfo":null,"standard":null,"dataylurl":null,"flag":null,"publishtime":null,"createby":null,"createtime":null,"ext1":null,"samplestoreloc":null,"hosturl":null,"datasize":null,"industryPlan":null,"keyInformation":"","samplePresentation":[],"officialSummary":"This dataset contains facial expression recognition data from 821 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.","dataexampl":null,"datakeyword":["driver expression recognition dataset","driver monitoring dataset","in-cabin monitoring data","driver drowsiness detection dataset","facial expression recognition dataset","automotive AI dataset","multimodal driver dataset","RGB infrared driver data","driver fatigue detection dataset","driver attention monitoring"],"isDelete":null,"ids":null,"idsList":null,"datasetCode":null,"productStatus":null,"tagTypeEn":"Task Type,Modalities","tagTypeZh":null,"website":null,"samplePresentationList":null,"datazyList":null,"keyInformationList":null,"dataexamplList":null,"bgimg":null,"datazyScriptList":null,"datakeywordListString":null,"sourceShowPage":"computer","dataShowType":"[{\"code\":\"0\",\"language\":\"ZH\"},{\"code\":\"1\",\"language\":\"ZH\"},{\"code\":\"2\",\"language\":\"EN,JP,PT,DE,KO,FR,ES\"},{\"code\":\"3\",\"language\":\"EN\"},{\"code\":\"4\",\"language\":\"JP\"}]","productNameEn":"821 Drivers - 7 Expression Recognition Data","BGimg":"","voiceBg":["/shujutang/static/image/comm/audio_bg.webp","/shujutang/static/image/comm/audio_bg2.webp","/shujutang/static/image/comm/audio_bg3.webp","/shujutang/static/image/comm/audio_bg4.webp","/shujutang/static/image/comm/audio_bg5.webp"]}
https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp
[]
Driver Monitoring Dataset – 7 Facial Expressions from 821 Drivers
driver expression recognition dataset
driver monitoring dataset
in-cabin monitoring data
driver drowsiness detection dataset
facial expression recognition dataset
automotive AI dataset
multimodal driver dataset
RGB infrared driver data
driver fatigue detection dataset
driver attention monitoring
This dataset contains facial expression recognition data from 821 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.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
![Specifications]()
Specifications
Population distribution
gender distribution: 374 males, 447 females; race distribution:Vietnam, Indonesia; age distribution: 18~45 years old, 46~60 years old, over 60 years old
Collecting environment
in-car Cameras
Data diversity
multiple expressions, multiple ages, multiple time periods
Device
visible light and infrared binocular camera, resolution 1,920x1,080
Shooting position
the center of the inside rear view mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position, rearview mirror wide angle lens position
Collecting time
day, evening, night
Vehicle Type
Car, SUV, MVP, Truck, Bus
Data Format
the video data format is .mp4
Accuracy
according to the accuracy of each person's acquisition expression, the accuracy exceeds 95%;the accuracy of label annotation is not less than 95%
![Sample]()
Sample
![Recommended Datasets]()
Recommended Dataset
Tell Us Your Special Needs
What types of computer vision applications can Nexdata’s datasets support?
Nexdata’s computer vision datasets support a wide range of AI applications, including image classification, object detection, image segmentation, facial and human-related recognition, scene understanding, autonomous driving, and other visual perception tasks. Depending on the dataset, data may include images, videos, bounding boxes, polygons, keypoints, segmentation masks, text annotations, and other structured labels.
Can Nexdata customize Computer Vision datasets based on our specific requirements?
Yes. If our off-the-shelf Computer Vision datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, annotation, and curation services. We can customize data based on your target objects, environments, scenarios, camera specifications, geographic locations, data volume, annotation formats, and quality standards to support specific model training and evaluation needs.
How does Nexdata ensure the quality and scalability of its Computer Vision datasets?
Nexdata applies multi-stage quality control throughout data collection, annotation, validation, and delivery. Depending on project requirements, we can implement customized annotation guidelines, multi-level reviews, consistency checks, and quality sampling to ensure dataset accuracy and consistency. Our data collection and processing capabilities can also be scaled to support large-volume Computer Vision projects.
317a9da3-fdae-4fff-a5ce-92fcfe3c546d
Specifications
Sample
Recommended Dataset
