Ph.D. defense on "human re-identification using soft biometrics in video surveillance". The presentation includes two parts: Part 1: simplified camera calibration Part 2: human re-identification using soft biometrics

1. Human Re-identification using Soft
Biometrics in Video Surveillance
Ph.D. Dissertation
Student: Shengzhe Li (이성철)
Supervisor: Prof. Hale Kim
May 22, 2015
1

2. Table of contents
• Introduction to soft biometrics
• Scene geometry and human height estimation
• Simplified camera calibration
• Simplified camera calibration with distortion correction
• Human descriptors and re-identification
• Combining motion and tracking-by-detection
• Automatic height estimation
• Feature-level color normalization
• Re-identification using soft biometrics
• Conclusion
• Achievement
2

3. Introduction to Soft Biometrics
3

4. Biometric technology map
4
Comparison of various biometric technologies in a
perspective of sensing distance and accuracy

5. Recent research activities on Soft Biometrics
5
2014.11.30 JIVP, Special Issue on Soft Biometrics:
Extraction and Applications based on Images and Videos
2015.1.15 PRL, Special Issue on Soft Biometrics
2014.9.6 ECCV, First International
Workshop on Soft Biometrics

6. Definition
Encyclopedia of Biometrics
“Any anatomical or behavioral characteristic that provides some information about the
identity of a person, but does not provide sufficient evidence to precisely determine
the identity can be referred to as a soft biometric trait. Personal attributes like gender,
ethnicity, age, height, weight, eye color, scars, marks, tattoos, and voice accent
are examples of soft biometric traits. ”
6
“a new form of biometric
identification which use physical or
behavioral traits that can be
naturally described by humans”
Daniel A. Reid Mark S. Nixon
Jean-Luc Dugelay
“the soft biometric traits instances are
created in a natural way, used by humans to
distinguish their peers.”
Antitza Dantcheva

7. State of the art technologies
7
Methods and performance

8. Domain of application
• Type I: Fusion with classical biometric traits
To integrate the information provided by soft biometric signatures with
the ones of a primary biometric system.
Framework of integration of soft biometrics to improve the accuracy of classical
biometric systems [Dantcheva2010]
8

9. State of the art performance (type I)
• Build a tunnel to collect soft labels are manually
• Performance of face recognition is increased by fusing
soft labels
• Soft labels are more useful at far distance
9
[Tome2014]

10. Domain of application
• Type II: Pruning the search
The soft biometric signature is used as a side information to filter the
original dataset W and to find a subset of the dataset Z.
Framework of integration of soft biometrics to improve the search efficiency of classical
biometric systems [Dantcheva2010]
10

11. intuVision
• Focuses on categorical classification of people
from ordinary video
• face-based gender and ethnicity classification
• gender, ethnicity, age and size
11
http://www.intuvisiontech.com/

12. Domain of application
• Type III: Human identification (re-identification)
The identification approach is based on a signature composed by soft
biometric traits, which can be extracted from images or videos
The scheme presents the
design of an identification
system based on soft
biometric traits.
[Dantcheva2010]
Very few studies
are performed in
this type
12

13. State of the art performance (type III)
13
[Dantcheva2010]
• Defines of two novel traits: weight and clothes
color
• Proposes a human identification based solely
on soft biometric traits
Collision probability of the proposed
BoSB in an N sized authentication
group with N ranging from 0 to 1,000
(a), and a magnified version in [0 100].

14. Soft Biometric Traits
14
Table of soft biometric traits [Tome2014]

15. Soft Biometric Traits
15
Table of soft biometric traits [Jaha2014]
Some commonly used soft biometric traits are
height, weight, age, ethnicity and gender, skin color, hair color and cloth color.

16. Two fundamental problems
• Scene geometry
• Scene geometry (camera calibration) is
fundamental to obtain soft biometric
features such as height and weight in
video surveillance
• Camera calibration is very complicated
and tedious because it needs the
measurements in real-world
• Color consistency
• Appearance of human changes within
one camera
• Color property changes very much
between indoor and outdoor cameras
16
Camera calibration problem
Color inconsistency problem

17. Scope
• Study on theory and application of soft biometrics and person
re-identification
• Propose a soft biometrics framework that can be accommodated
in video surveillance
• Discover the illumination changes between indoor and outdoor
under various weather condition
• Collect a real-world dataset for evaluating the system
Soft Biometrics
Person Re-
identification
Database
Defines prototype and
framework
Data storage and
search
Feature extraction and
matching methods
17

18. Proposed method
• Part 1. Scene geometry and human height estimation
Measure the physical size of human in the image
• Simplified camera calibration
• Simplified camera calibration with distortion correction
• Part 2. Human descriptors and re-identification
Extract consistent features from human body
• Combining motion and tracking-by-detection
• Automatic height estimation
• Feature-level color normalization
• Re-identification using soft biometrics
18

19. Part 1. Scene geometry and human height
estimation
19
Simplified camera calibration

20. Problems
Camera calibration experiments performed by Bin et al. at CVLab in 2011.
(Left) vanishing point based method. 8 points, height H, distance D1, D2.
(Right) DLT method. As many as possible points.
These method are accurate, but complicated and tedious!
It is natural to associate a walking or standing human with the camera
calibration problem in the context of video surveillance
20

21. Typical installation of camera
21
Indoor
Outdoor
Most cameras for video surveillance are
installed in high positions with a slightly tilted
angle to ensure the best field of view.

22. Main ideas
• The reason why camera calibration is complicated is that
there are too much calibration parameters (five intrinsic and
six extrinsic parameters).
• Reducing the number of calibration parameters can simplify
the problem.
• Considering that most cameras for video surveillance are
installed in high positions with a slightly tilted angle
• It is possible to retain only three calibration parameters in
the original camera model, namely the focal length, tilting
angle and camera height.
22

23. Coordinate system, notations
The typical camera installation and the
coordinate system in video surveillance.
23
Definition Notation
The world coordinates [X,Y,Z]T
The image coordinates [x,y]T
Head points in world [Xh,Yh,Zh]T
Head points in image [xh,yh]T
Foot points in world [Xf,Yf,Zf]T
Foot points in image [xf,yf]T
Focal length f
Tilt angle θ
Camera height c

24. Simplified Calibration
Most cameras for video surveillance are installed in high positions with a slightly
tilted angle. In such installation, the rotation angles along axis Y and Z can be
assumed as 0 (which are also known as pan and roll), as well as the translations
along axis X and Z. Therefore,
• 𝑃 =
𝑓 0 0
0 𝑓 0
0 0 1
1 0 0
0 cos 𝜃 − sin 𝜃
0 sin 𝜃 cos 𝜃
1 0 0
0 1 0
0 0 1
0
𝑐
0
=
𝑓 0
0 𝑓 cos 𝜃
0 sin 𝜃
0 0
−𝑓 sin 𝜃 𝑐𝑓 cos 𝜃
cos 𝜃 𝑐 sin 𝜃
24

25. Simplified Calibration
These three parameters can determine the mapping from the world coordinates
[X,Y,Z]T to the image coordinates [x,y,w]T as
x
y
ω
= 𝑃
X
Y
Z
1
=
𝑓 0
0 𝑓 cos 𝜃
0 sin 𝜃
0 0
−𝑓 sin 𝜃 𝑐𝑓 cos 𝜃
cos 𝜃 𝑐 sin 𝜃
X
Y
Z
1
=
𝑓X
𝑓Y cos 𝜃 − 𝑓Z sin 𝜃 + 𝑐𝑓 cos 𝜃
Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃
which can be represented in Cartesian coordinates as
x
y =
𝑓X
Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃
𝑓Y cos 𝜃 − 𝑓Z sin 𝜃 + 𝑐𝑓 cos 𝜃
Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃
25

26. Simplified Calibration
A basic relationship between the world coordinates Y, Z and the image coordinates y, which is
given as
y =
𝑓Y cos 𝜃 − 𝑓Z sin 𝜃 + 𝑐𝑓 cos 𝜃
Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃
=
𝑓Y − 𝑓Z tan 𝜃 + 𝑐𝑓
Y tan 𝜃 + Z + 𝑐 tan 𝜃
.
Since each pair of the head and foot of the y coordinates, denoted as yh and yf, can be
measured from the image. By above Eq., a set of equations with three unknowns can be built
as
yf =
−𝑓Z tan 𝜃+𝑐𝑓
Z+𝑐 tan 𝜃
yh =
𝑓Yh−𝑓Z tan 𝜃+𝑐𝑓
Yh tan 𝜃+Z+𝑐 tan 𝜃
.
Eliminating Z,
yh =
𝑓 𝑐tan2 𝜃+Yh+𝑐 yf+𝑓2 tan 𝜃Yh
tan 𝜃Yhyf+𝑓(tan2 𝜃Yh+𝑐tan2 𝜃+𝑐)
.
26
Foot y, real height
 head y

27. Simplified Calibration
The parameters can be found by the nonlinear regression as
𝑓
𝜃
𝑐
= argmin
𝑓,𝜃,𝑐
𝑖=1
𝑁
(yh𝑖 − yh𝑖)2
.
Once the calibration parameters of a camera are obtained, the physical height of a
person can be estimated from a pair of head and foot points observed from the
image.
Yh =
𝑓𝑐 tan2 𝜃+1 (yf−yℎ)
tan 𝜃yhyf−𝑓yf+𝑓 tan2 𝜃yh−𝑓2tan 𝜃
.
27
Foot y, head y
 real height

28. Parameter optimization
A scatter plot of the y coordinates of the observed and estimated head points with
respect to the observed foot points. The initial parameters f = 720, θ = -30 and c = -300
are approximated via visual estimation and the optimal parameters are found as f =
547.7, θ = -38.6 and c = -270.2 by the nonlinear regression method.
28
Optimal parameters can be found by the nonlinear regression.

29. Dataset for evaluation
• Number of subjects: 11
• Number of cameras: 9
• Video resolution: 1280 × 1024
• Location: Inha Univ. Hitech Bldg.
• Mark: Manual
29

30. Experimental results
30
Walking human based evaluation.
Ruler based evaluation

31. Comparison
Calibration
object
Mean
absolute error
Standard
deviation
Maximum
error
Krahnstoever Walking human 5.80% N/A N/A
Lee Cubix box or line N/A N/A 5.50%
Gallagher Grid pattern N/A 2.67cm 3.28cm
Jeges Grid pattern 2.03cm 4.17cm N/A
Proposed Walking human
1.39cm
(0.80%)
1.91cm
(1.1%)
7.93cm
(4.5%)
Comparison of the proposed method with the existing height estimation
methods
31

32. Part 1. Scene geometry and human height
estimation
32
Simplified camera calibration with distortion
correction

33. Camera distortion problem
181cm169cm158cm
GT : 170cm
33

34. Floor length estimation problem
GT : 396cm
AR : 322cm
GT : 271cm
AR : 278cm
34

35. The 4th order distortion model
• The types of distortion (from Wikipedia)
Barrel distortion
Pincushion
distortion
35

36. The 4th order distortion model
Camera distortion can usually be expressed as
𝑥 𝑑 = 𝑥 𝑢 1 + 𝑘 𝑑1 ∙ 𝑟𝑢
2
+ 𝑘 𝑑2 ∙ 𝑟𝑢
4
𝑦 𝑑 = 𝑦𝑢 1 + 𝑘 𝑑1 ∙ 𝑟𝑢
2 + 𝑘 𝑑2 ∙ 𝑟𝑢
4
where 𝑥 𝑢, 𝑦𝑢 are undistorted(ideal) coordinates, 𝑥 𝑑, 𝑦 𝑑 are distorted
coordinates(real) and 𝑟𝑢 is the radius. 𝑘 𝑑1 and 𝑘 𝑑2 are the distortion
parameters.
The inverse of camera distortion model has same form but different
coefficients
𝑥 𝑢 = 𝑥 𝑑 1 + 𝑘 𝑢1 ∙ 𝑟𝑑
2
+ 𝑘 𝑢2 ∙ 𝑟𝑑
4
𝑦𝑢 = 𝑦 𝑑 1 + 𝑘 𝑢1 ∙ 𝑟𝑑
2
+ 𝑘 𝑢2 ∙ 𝑟𝑑
4 .
36

37. Distort (original) image
37

38. Undistort image
38

39. Distort image
39

40. With distortion correction
AR: 170cm AR: 169cm AR: 174cm
GT : 170cm
40
* AR : Algorithm Result, GT : Ground Truth

41. With distortion correction
AR : 357cm
GT : 396cm
AR : 278cm
GT : 271cm
41
* AR : Algorithm Result, GT : Ground Truth

42. Distortion correction results
42
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
w/o distortion correction w/ distortion correction
Calibration error w/ and w/o distortion correction
Errordistrubution(meter)
The calibration error with and without distortion correction. The
standard deviation is reduced from 3.73cm to 2.45cm after
applying distortion correction.

43. Conclusion
• The proposed method requires neither any special
calibration object nor a special pattern on the ground,
such as parallel or perpendicular lines; it does not rely on
computing the vanishing points, which is difficult to
estimate in practice
• The proposed method can be integrated with automated
human detection methods to perform full autocalibration.
This remains as a future study.
• Lens distortion correction offers more accurate height
estimation especially for boarder area as well as the length
in the floor.
43

44. Open source on Github (GPLv2 license)
https://github.com/lishengzhe/ccvs
44

45. Open source on Github (GPLv2 license)
45
42 visitors after two weeks

46. Open source on Matlab
46
40 downloads per month

47. Part 2. Human descriptors and re-identification
47

48. Human re-identification
Human re-identification consists in recognizing an individual in diverse
locations over different non-overlapping camera views, considering a large
set of candidates [Farenzena2010].
48
VIPeR dataset
Cam_A
Cam_B
3DPeS dataset

49. Approaches
• Three different re-identification
approaches
• Global features
• 2D body models
• 3D body models
• Features
• Global  various types of color
histogram
• Local  region and patch based
descriptor, such as MSCR
• Combination of features
• Feature level
• Score level
49
Three different re-identification
approaches [Balteri2014]

50. Main ideas
• Most re-identification methods only relay on
image features such as color and texture
• Appearance of human might change within
same camera because of wind or walking
direction
• Color property changes extremely between
indoor and outdoor cameras
• Re-identification using color and soft
biometric trait can be measured in video
surveillance such as height
• Tracking based approach for feature
extraction
• Feature-level color normalization for constant
color extraction
50
Color inconsistency problem
Appearance of the human might change

51. Part 2. Human descriptors and re-identification
51
Human detection and tracking

52. HOG on mini motion map
• Requirements
• Processing time
• Stable bounding box
• Minimum false detection
• Proposed method
• Compute motion map by
GMM and convert it to the
mini motion map
• Compute HOG when mini
motion map is none zero
Mini motion map for reducing
unnecessary computation in the
HOG based human detection.
Flowchart of the
proposed approach
combining motion
and tracking.
Method Time
Original HOG 60ms
HOG on mini motion map
(sparse)
20ms
HOG on mini motion map
(crowed)
30ms
Comparison of processing time
(i7 3.4G 4cores, @960 x 540)

53. Human detection and tracking (demo)
53

54. Part 2. Human descriptors and re-identification
54
Human feature extraction

55. Automatic height estimation
• Camera calibration allow us to estimate height of human
• Steps for height estimation
Background
subtraction
Rotate ROI
Profile
histogram
Head foot
points
extraction
Height
computation
head
foot
[Lv2006]

56. Automatic height estimation
For a 2D image the raw image moments 𝑀𝑖𝑗 with pixel intensities
𝐼(𝑥, 𝑦) are calculated by
𝑀𝑖𝑗 =
𝑥 𝑦
𝑥 𝑖
𝑦 𝑖
𝐼(𝑥, 𝑦)
The components of the centroid are
𝑥 =
𝑀10
𝑀00
and
𝑦 =
𝑀01
𝑀00
.
Information about image orientation can be derived by first using
the second order central moments to construct a covariance matrix.
𝜇20
′
= 𝜇20/𝜇00 = 𝑀20/𝑀00 − 𝑥
2
𝜇02
′
= 𝜇00/𝜇00 = 𝑀02/𝑀00 − 𝑦
2
𝜇11
′
= 𝜇11/𝜇00 = 𝑀11/𝑀00 − 𝑥𝑦
http://en.wikipedia.org/wiki/Image_moment

57. Automatic height estimation
The covariance matrix of the image 𝐼 𝑥, 𝑦 is now
cov 𝐼 𝑥, 𝑦 =
𝜇20
′
𝜇11
′
𝜇11
′
𝜇02
′ .
The eigenvectors of this matrix correspond to the major and
minor axes of the image intensity, so the orientation can thus
be extracted from the angle of the eigenvector associated with
the largest eigenvalue. It can be shown that this angle θ is
given by the following formula:
θ =
1
2
tan−1
2𝜇11
′
𝜇20
′
− 𝜇02
′
The blob is rotated around centroid point (x, y) by angle θ.
http://en.wikipedia.org/wiki/Image_moment

58. Automatic height estimation
58

59. Automatic height error
121110987654321
2.0
1.9
1.8
1.7
1.6
1.5
Subject
Height
Height extimation (auto)
Automatic height estimation error, slash lines represent GT.

60. Color feature
• Comparison of color spaces
10 20 30 40 50 60 70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cumulative Matching Characteristic (CMC) Curves - VIPeR dataset
Matches
Rank
HSV
Lab
RGB
HSV+Lab
HSV+RGB
Lab+RGB
HSV+Lab+RGB
HSV outperform the other color spaces
Combine HSV with the other color space degrade performance

61. Color property between cameras
Camera 01, outdoor, uniform light Camera 04, indoor, backlight
1 16 32 36
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
HSV histogram bins (16:16:4)
Meanfeature
Upper body HSV feature
0 16 32 36
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
HSV histogram bins (16:16:4)
Meanfeature
Lower body HSV feature
0 16 32 36
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
HSV histogram bins (16:16:4)Meanfeature
Upper body HSV feature
0 16 32 36
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
HSV histogram bins (16:16:4)
Meanfeature
Lower body HSV feature
Comparison of mean HSV feature shows difference between cameras

62. Proposed method
• Feature-level color normalization
• Assuming the means of color features are equal at same time
𝑓𝑚→𝑛(𝑥) = 𝑥 + ∆ 𝑚→𝑛
𝑛∈𝑁
𝑥 𝑛 =
𝑚∈𝑀
𝑓𝑚→𝑛(𝑥 𝑚) =
𝑚∈𝑀
𝑥 𝑚 + ∆ 𝑚→𝑛
∆ 𝑚→𝑛=
𝑛∈𝑁
𝑥 𝑛 −
𝑚∈𝑀
𝑥 𝑚
• Advantages
• No need pixel by pixel mapping
• Works for any color space

63. Experimental results
• Color
• Color + height
• Normalized color
• Normalized color + height

64. Dataset
Date of
recording
Length of
recording
Weather
Number of
cameras
Number of
subjects
Video format
13/11/07 00:17:00 Cloud 5 7 1280×720
15/03/21 00:25:00 Sunny 7 12 1280×1024
15/04/04 00:23:00 Cloud 7 11 1280×1024
15/04/11 00:17:00 Sunny 7 15 1280×1024
13/11/07 15/03/28 15/04/04 15/04/11

65. Interactive performance evaluation tool
for re-identification
GUI of the interactive performance evaluation tool for
re-identification
Features:
• Select dataset
• Select cam
• Select person
• Annotate GT
• CMC for cam
• CMC for all

66. Re-id result (131107, cam 1 2 4 5)
2 4 6 8 10 12 14 16 18
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve
Rank
IdentificationAccuracy
Color
Color + Height
Re-identification result on dataset 131107 (cloudy)
13/11/07

67. 67
5 10 15 20 25 30 35 40 45 50 55
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve
Rank
IdentificationAccuracy
Color
Color + Height
Re-identification result on dataset 150328 (sunny)
15/03/28
Re-id result (150328, cam 1 2 4 5)

68. 68
5 10 15 20 25 30
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve
Rank
IdentificationAccuracy
Color
Color + Height
15/04/11
Re-id result (150411, cam 1 2 4 5)
Re-identification result on dataset 150411 (sunny)

69. Re-id result (150404, cam 1 2 5 6)
5 10 15 20 25 30
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve
Rank
IdentificationAccuracy
Color
Color + Height
Re-identification result on dataset 150404 (cloudy)
15/04/04

70. Re-id result (150404, cam 1 2 5 6)
5 10 15 20 25 30
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve
Rank
IdentificationAccuracy
Color
Color + Height
82.98  87.94
58.16  53.19
92.2  95.05
Re-identification result on dataset 150404

71. Re-id result (131107, cam04)
2 4 6 8 10 12 14 16 18 20 22 24
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve (cam04)
Rank
IdentificationAccuracy
Color
Color+Height
N Color
N Color+Height

72. Re-id result (150328, cam04)
20 40 60 80 100 120 140 160
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve (cam04)
Rank
IdentificationAccuracy
Color
Color+Height
N Color
N Color+Height

73. Re-id result (150404, cam04)
20 40 60 80 100 120 140 160
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve (cam04)
Rank
IdentificationAccuracy
Color
Color+Height
N Color
N Color+Height

74. Re-id result (150411, cam04)
20 40 60 80 100 120 140 160 180
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CMC Curve (cam04)
Rank
IdentificationAccuracy
Color
Color+Height
N Color
N Color+Height

75. Conclusion and future works
• Conclusion
• Simplified camera calibration using walking human is very practical
and results show promising results
• Proposed tracking based re-identification provide more rich
information than image based ones
• Feature-level color normalization can balance color between
cameras in an efficient way
• Re-identification performs better in a cloudy day
• Future works
• More soft biometric traits can be added such as body profile, gait
frequency, gait speed
• Depth, 4K camera might provide more details about human and
some soft biometric traits like hair color and skin color can be
extracted
• On-line system is required to evaluate the proposed method in real-
world application
75

76. Contributions of this study
• Simplified camera calibration
• Simplified camera calibration significantly reduce time and effort
for camera calibration that has a benefit for many video surveillance
applications
• Tracking based approach for re-identification
• HOG on mini motion map provides two time faster processing time
while maintains same performance
• Tracking based feature extraction is more robust to appearance
change in one camera
• Feature-level color normalization
• Feature-level color normalization can improve the color
consistency between cameras
• Re-identification using soft biometrics
• Combining color and height for person re-identification
improves existing method which mainly based on color only
76

77. Achievement
• Book chapter
1. Hale Kim, Shengzhe Li, “Characterization and Measurement of
Difficulty for Fingerprint Databases for Technology Evaluation,”
Encyclopedia of Biometrics, Springer US, 2014
• International journal
1. X Cui, H Kim, S Li, KS Kwack, BH Min, Extraction of anatomical
landmarks and axis for 3D coordinate system construction of
femur and tibia bone models, Osteoarthritis and Cartilage 23,
A246-A248 (IF:4.663)
2. Shengzhe Li, Van Huan Nguyen, Mingjie Ma, Cheng-Bin Jin,
Trung Dung Do and Hakil Kim, “A Simplified Camera Calibration
Method for Human Height Estimation in Video Surveillance,”
EURASIP Journal of Image and Video Processing (IF: 0.66, 2nd
review)
3. Thi Ly Vu, Trung Dung Do, Cheng-Bin Jin, Shengzhe Li, Van
Huan Nguyen, Hakil Kim, and Chongho Lee, “Improvement of
Accuracy for Human Action Recognition by Histogram of
Changing Points and Average Speed Descriptors,” Journal of
Computing Science and Engineering, Vol. 9, No. 1, March 2015,
pp. 29-38
4. Shengzhe Li, Hakil. Kim, Changlong. Jin, S. Elliott, and Mingjie.
Ma, "Assessing the level of difficulty of fingerprint datasets based
on relative quality measures," Information Sciences, 268 (2014)
122–132. (IF: 3.89)
77

78. Achievement (continued)
• International journal (continued)
5. Xuenan. Cui, Shengzhe. Li, Miao. Yu, Hakil. Kim, K.-S. Kwack, and B.-H. Min, "Automatic
cartilage segmentation and measurement for diagnosis of OA using 3D box and Gaussian
filters," Osteoarthritis and Cartilage, vol. 21, pp. S232-S233, 2013. (IF: 4.663)
6. Naw Chit Too June, Xuenan Cui, Shengzhe Li, Hakil Kim and Kyu-Sung Kwack, "Fast and
Accurate Rigid Registration of 3D CT Images by Combining Feature and Intensity.", Journal of
Computing Science and Engineering, 2012, 6(1): 1-11.
• Domestic journals
1. Jung-min Kim, Shengzhe Li, Hak-il Kim, “2D- 3D Human Face Verification System based on
Multiple RGB-D Camera using Head Pose Estimation”, Journal of The Korea Institute of
Information Security & Cryptology, VOL.23, NO.6, Dec. 2013
2. Sheongwook Hong, Xuenan Cui, Shengzhe Li, Naw Chit Too June, Kyu-sung Kwak, Hakil Kim,
“Efficient denoising and segmentation of multi-echo knee MR images,” Journal of the Korean
institute of information scientists and engineers, software and application , Vol.38, No.68 pp.
340-346, June 2011
• International conference
1. Shengzhe Li, Xuenan Cui, Miao Yu, Hakil Kim, Kyu-sung Kwack, "Detecting and visualizing
cartilage thickness without a shape model", SMC 2012: 232-236
2. Shengzhe Li; Changlong Jin; Hakil Kim; Elliott, S., "Assessing the Difficulty Level of Fingerprint
Datasets Based on Relative Quality Measures", Hand-Based Biometrics (ICHB), 2011
International Conference on.
3. Changlong Jin; Shengzhe Li; Hakil Kim, "Type-Independent Pixel-Level Alignment Point
Detection for Fingerprints", Hand-Based Biometrics (ICHB), 2011 International Conference on.
4. Shengzhe Li, Xuenan Cui, Naw Chit Too June, Hakil Kim, Kyu-sung Kwack: Label-guided snake
for bone segmentation and its application on Medical Rapid Prototyping. SMC 2011: 752-757
78

79. Achievement (continued)
• International conference (continued)
6. Seongwook Hong, Xuenan Cui, Shengzhe Li, Naw Chit Too June, Kyu-sung Kwack, Hakil Kim,
“Noise removal for multi-echo MR images using global enhancement”. SMC 2010: 3616-3621
7. Changlong Jin, Shengzhe Li, Hakil Kim, Eunsoo Park, “Fingerprint Liveness Detection Based on
Multiple Image Quality Features”, WISA 2010: 281-291
• Patent
1. 수술 영상의 실시간 시각적 노이즈 자동 제거 장치, 방법 및시스템, 2014.2, 등록
2. 복수의 카메라들을 이용한 3D 얼굴 모델링 장치, 시스템 및 방법, 2013. 9, 등록
3. ＧＰ－ＧＰＵ를 이용한 3Ｄ 의료 영상 정합의 병렬처리방법, 2012. 8, 등록
4. 영상감시 시스템을 위한 카메라 감시 영역 산출 장치 및 방법, 2014.7, 출원
5. 외부 환경 정보를 반영한 비디오 보정 시스템 및 방법, 2014.3, 출원
6. 무릎뼈의 3차원 좌표 시스템 구축 장치 및 방법, 2013. 8, 출원
79

80. Achievement (continued)
• Research project
• 지문인식
• 바이오인식 데이터의 보안 및 성능평가 기술 국제표준 등록, 한국산업기술진흥원,
2009~2012
• 바이오인식시스템 성능시험용 DB기준 및 구성방안 마련, 한국인터넷진흥원, 2010~2011
• 제품기반의 지문인식시스템 성능 및 웹기반 BioAPI 표준적합성 시험환경 구축,
한국인터넷진흥원, 2012~2013
• 의료영상
• 골관절염의 진단 및 치료반응평가를 위한 MRI 및 CT 영상 후처리 알고리즘의 개발,
아주대학교산학협력단, 2009~2014
• 지능형 영상감시
• 스마트 카메라를 위한 영상분석 기술 및 사람인식 성능평가 방법/표준 개발-원거리 사람인식
기술의 성능평가 방법 및 표준화, 한국전자통신연구원, 2011~2014
• 다수의 고정형 카메라 기반 특정 보행자 추적 기술 개발, 방송통신위원회, 2013~2015
80

81. Thank you for your attention
81
Q&A