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

Face recognition
Face occlusion
Multi-pose per person
Face with mask
Multiple light conditions
Multiplescenes
blockage
closure
stoppage
block
stop
obstruction
blocking
occluded
front
occlusive
check
closing
embolism
apoplexy
shutdown
hindrance
blockade
thrombosis
impaction
tampons
arrest
close
congestion
embolus
fastener
hitch
obturation
seal
stopper
abocclusion
blocks
clog
clot
clotting
constipation
holdup
impediment
occludent
plug
stoppages
stopples
stops
tampon
thrombus
airlock
barrier
cap
catch
clogging
cork
plugging
posture
perplex
puzzle
mystify
nonplus
bewilder
gravel
flummox
position
baffle
amaze
dumbfound
masquerade
beat
stick
stupefy
impersonate
attitude
place
stance
model
present
affectation
mannerism
attitudinize
sit
put
submit
show
airs
front
propose
suggest
pretense
propound
affectedness
raise
strike
a
pose
constitute
facade
personate
show
off
advance
pretend
act
bluff
arrange
put
on
airs
peacock
posing
confront
look
meet
front
facing
surface
encounter
side
brave
grimace
experience
visage
address
veneer
countenance
tackle
cover
oppose
confronting
defy
expression
aspect
appearance
cheek
watch
challenge
nerve
font
overlook
endure
withstand
suffer
brass
cope
with
dial
head
exterior
typeface
handle
undergo
be
facing
facade
face
up
facial
expression
physiognomy
beard
boldness
outside
deal
faces

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.rn

Paid Datasets
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
SpecificationsSpecifications
Data size
2,937 people, 200 images per person
Race distribution
Asian(Vietam .etc.)
Gender distribution
1,480 males , 1,457 females
Age distribution
ranging from teenager to the elderly, the middle-aged and young people are the majorities
Collecting environment
including indoor and outdoor scenes
Data diversity
multiple face poses, multiple occlusion cases, multiple ages, multiple light conditions and scenes
Device
cellphone
Data format
.jpg
Accuracy
the accuracy of labels of face pose, occlusion case, gender and age is more than 97%
Sample Sample
  • Waiting For Data
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