ldp local directional pattern & ldn local directional number pattern

LDP Local Directional Pattern &LDNLocal Directional Number Pattern

报告人：黄倩颖

内容两种局部编码模式构造描述子

LDP Local Directional Pattern LDN Local Directional Number Pattern

对 Local Binary Pattern (LBP)的改良

Descriptor

geometric-feature-based appearance-based

Part One

作者简介

文章结构

方法概述

讲解提纲• LBP方法回顾• LDP的创新点• LDP的鲁棒性• LDP的旋转不变性• 实验• 结论

作者简介

Local Directional Pattern (LDP) – A Robust Image Descriptor for Object RecognitionTaskeed Jabid, Md. Hasanul Kabir, Oksam Chae Department of Computer Engineering Kyung Hee University, Republic of Korea

2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance

Taskeed Jabid

Human Computer Interaction, Computer Vision, Object Recognition

Local Directional Pattern (LDP) for face recognition

International Conference Consumer Electronics (ICCE), 2010

Cited by 44

文章结构

• Introduction• LDP image descriptor

a. Local Binary Pattern (LBP)b. Local Directional Pattern (LDP)c. Robustness of LDPd. Rotation invariant LDPe. LDP Descriptor

• Texture classification using LDP descriptor• Face recognition using LDP descriptor• Conclusions

Abstract

LDP( Local Directional Pattern) is a local feature descriptor for describing local image feature.• Though LBP is robust to monotonic illumination

change but it is sensitive to non-monotonic illumination variation and also shows poor performance in the presence of random noise • A LDP feature is obtained by computing the

edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation.

Part One

作者简介

文章结构

方法概述

讲解提纲• LBP方法回顾• LDP的创新点• LDP的鲁棒性• LDP的旋转不变性• 实验• 结论

讲解提纲

• LBP方法回顾• LDP的创新点• LDP的鲁棒性• LDP的旋转不变性• 实验• 结论

Local Binary Pattern (LBP)

Original LBP

85 32 26

53 50 10

60 38 45

1 0 0

1 0

1 0 0

Threshold 50

（ 0 0 1 1 1 0 0 0 ） 2 = 5 6

26 < 50 0

Local Directional Pattern (LDP)

Kirsch masks

North- East

North

North-West M

2M1

M4

M0

M5

M6

M7

M3

East

South

West

South-West

South-East

-3 -3 5

-3 0 5

-3 -3 5

5 5 5

-3 0 -3

-3 -3 -3

-3 5 5

-3 0 5

-3 -3 -3

5 -3 -3

5 0 -3

5 -3 -3

M3 M2 M1

M4 M0

M5 M6 M7

5 5 -3

5 0 -3

-3 -3 -3

-3 -3 -3

5 0 -3

5 5 -3

-3 -3 -3

-3 0 -3

5 5 5

-3 -3 -3

-3 0 5

-3 5 5

85

32

26

53

50

10

60

38

45

399

Computing…

85 32 26

53 50 10

60 38 45

313 97 503

537 399

161 97 161

Kirsch masks

0 0 1

1 1

0 0 0

LDP Binary Code =00010011LDP Decimal Code=19

LDPk

k=3

19

Robustness of LDP

noise & non-monotonic illumination changes

85 32 26

53 50 10

60 38 45

85 32 26

53 50 10

60 38 45

-4 -3 -6

-15 +8 +5

+5 +5 +2

81 29 32

38 58 15

65 43 47

LBP = 00111000LDP = 00010011

LBP = 00101000LDP = 00010011

Rotation invariant LDP

85 32 26

53 50 10

60 38 45

0 0 1

1 1

0 0 0

26 10 45

32 50 38

85 53 60

1 1 0

0 0

0 1 0

313 97 503

537 399

161 97 161

503 393 161

97 97

313 537 161

Rotation Invariant LDP Code = 00110001

LDP Descriptor

Accumulating the occurrence of LDP feature

Experiments

Texture Classification using LDP histogram

Primary pictures from Brodatz texture album:(a) Bark,(b) Brick, (c) Bubbles,(d) Grass, (e) Leather, (f) Pigskin, (g) Raffia, (h) Sand, (i) Straw, (j) Water, (k) Weave, (l) Wood and (m) Wool

Experiments

Texture Classification using LDP histogram

Experiments

Extracted rotation invariant LDP features of each pixel of the image then combined to generate rotation invariant image descriptor using LDP histogram following equation.

Experiment Results

The accuracy of the method

Results

Face recognition using LDP descriptor

(a) fa set, used as a gallery set, contains frontal images of

1,196 people.

(b) fb set (1,195 images) with an alternative facial expression

than in the fa photograph.

(c) fc set (194 images) taken under different lighting

conditions.

(d) dup I set (722 images) taken later in time.

(e) dup II set (234 images) subset of the dup I set containing

images that were taken at least a year after the

corresponding gallery image.

Database FERET

Face recognition using LDP descriptor

Classification using LDP histogram

Template matching

Experiment Results

Part Two

作者简介

文章结构

方法概述

讲解提纲• LBP LDP缺点• LDN 三个关键点• 人脸描述• 实验• 结论及未来工作

作者简介

Local Directional Number Pattern for Face Analysis: Face and Expression RecognitionAdin Ramirez Rivera,Student Member, IEEE,

Jorge Rojas Castillo,Student Member, IEEE,

and Oksam Chae,Member, IEEE

IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 5, MAY 2013

Cited by 2 | Year 2012 |

Adin Ramirez Rivera Image Processing, Computer Vision

Content-Aware Dark Image Enhancement through Channel Division

IEEE Transactions on Image Processing 21 (9), 3967-3980

Cited by 9 | Year 2012

文章结构

• Introduction• Local Directional Number Pattern• Difference With Previous Work• Coding Scheme• Compass Masks

• Face description• Face recognition• Conclusions

Abstract

A novel local feature descriptor

LDN encodes the directional information of the face’s textures in a compact way, producing a more discriminative code than current methods

Part Two

作者简介

文章结构

方法概述

讲解提纲• LBP LDP缺点• LDN 三个关键点• 人脸描述• 实验• 结论及未来工作

讲解提纲

• LBP LDP缺点• LDN 三个关键点•人脸描述•实验•结论及未来工作

LBP

The method discards most of the information in the neighborhood.

It limits the accuracy of the methodIt makes the method very sensitive to noiseMoreover, these drawbacks are more

evident for bigger neighborhoods

Directional (LDiP) & Derivative (LDeP)

Miss some directional information (the responses’ sign) by treating alldirections equally

Sensitive to illumination changes and noise, as the bits in the code will flip and the code will represent a totally different characteristic

Key points of LDN

LBP

Direction

number

Signinformatio

n

gradientinformati

on

6-bit

LDN

Key points of LDN

Direction

number

Signinformatio

n

gradientinformati

on

6-bit

LDN

Coding Scheme

Direction

number

Signinformatio

n

+ -+ -

Coding Scheme

Compass Masks

Two kinds of masks

𝐿𝐷𝑁𝐾

𝐿𝐷𝑁𝜎𝐺

derivative-Gaussian mask

Kirsch masks

Compass Masks

Kirsch masks

North- East

North

North-West M

2M1

M4

M0

M5

M6

M7

M3

East

South

West

South-West

South-East

-3 -3 5

-3 0 5

-3 -3 5

5 5 5

-3 0 -3

-3 -3 -3

-3 5 5

-3 0 5

-3 -3 -3

5 -3 -3

5 0 -3

5 -3 -3

M3 M2 M1

M4 M0

M5 M6 M7

5 5 -3

5 0 -3

-3 -3 -3

-3 -3 -3

5 0 -3

5 5 -3

-3 -3 -3

-3 0 -3

5 5 5

-3 -3 -3

-3 0 5

-3 5 5

Compass Masks

derivative-Gaussian mask

• Compute code in gradient space • Therefore, use Gaussian smoothing to

stabilize the code in presence of noise

Generate a compass mask,{M0σ,...,M7σ}, by rotating Mσ, 45°apart, in eight different directions

Compass Masks

derivative-Gaussian mask

Face Descriptor

Histogram

LH & MLH

Face Descriptor

Two kinds of descriptor

Code in LH

Code in MLH must be

Face Recognition

Chi-Square dissimilarity measure

Face recognition using LDP descriptor

(a) fa set, used as a gallery set, contains frontal images of

1,196 people.

(b) fb set (1,195 images) with an alternative facial expression

than in the fa photograph.

(c) fc set (194 images) taken under different lighting

conditions.

(d) dup I set (722 images) taken later in time.

(e) dup II set (234 images) subset of the dup I set containing

images that were taken at least a year after the

corresponding gallery image.

Database FERET

Experiment Results

small neighborhoods (3×3, 5×5, 7×7)medium neighborhoods (5×5, 7×7, 9×9) large neighborhoods (7×7, 9×9, 11×11)

Face recognition accuracy

Experiment Results

Noise EvaluationWith white Gaussian noise

Conclusion

• Combination of different sizes (small, medium and large) gives better recognition rates for certain conditions.

• Evaluated LDN under expression, time lapse and illumination variations, and found that it is reliable and robust throughout all these conditions.

总结及未来工作

•如何选择一个描述子• 长度• 描述精度• 抗噪能力• 计算强度

•如何设计一个描述子• 舍弃冗余的信息• 整合多种信息来源• 信息压缩

Thank you!