# LDP Local Directional Pattern & LDN Local Directional Number Pattern

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LDP Local Directional Pattern & LDN Local Directional Number Pattern

LDN Local Directional Number Pattern 对Local Binary Pattern (LBP)的改良

Descriptor geometric-feature-based appearance-based
geometric-feature-based –sparse 稀疏 appearance-based methods -dense 密集 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

Local Binary Pattern (LBP) Local Directional Pattern (LDP) Robustness of LDP Rotation invariant LDP 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. 非线性光 随机噪点 八个方向的边缘响应值 相对强度大小 考虑了周边的值，因此更具有鲁棒性

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

Local Binary Pattern (LBP)
Original LBP 26 < 85 32 26 53 50 10 60 38 45 1 Threshold 50 前情提要 选定一个位置，一个方向开始编码 （ ）2 = 56

Local Directional Pattern (LDP)
Kirsch masks North 5 -3 5 -3 -3 5 M3 M2 M1 North-West North- East 5 -3 M3 M2 M1 M4 M0 M5 M6 M7 399 M0 -3 5 85 32 26 53 50 10 60 38 45 M4 West East Keshk 加权求和 求各个方向趋势 -3 5 -3 5 -3 5 M5 M6 M7 South- West South- East South

19 Computing… LDPk Kirsch masks 85 32 26 53 50 10 60 38 45 k=3
313 97 503 537 399 161 Kirsch masks 19 LDPk k=3 1 K默认取3 从东边开始绕中心一周 LDP Binary Code = LDP Decimal Code= 19

Robustness of LDP noise & non-monotonic illumination changes -4 -3 -6
85 32 26 53 50 10 60 38 45 81 29 32 38 58 15 65 43 47 -4 -3 -6 -15 +8 +5 +2 85 32 26 53 50 10 60 38 45 重点：A Robust Image Descriptor Since edge responses are more stable than intensity values, LDP pattern provides the same pattern value even presence of noise and non-monotonic illumination changes. 优于LBP LBP = LDP = LBP = LDP =

Rotation invariant LDP
85 32 26 53 50 10 60 38 45 1 313 97 503 537 399 161 26 10 45 32 50 38 85 53 60 1 503 393 161 97 313 537 旋转不变性 总是从1开始 减少了描述子的个数 Rotation Invariant LDP Code =

LDP Descriptor Accumulating the occurrence of LDP feature 统计直方图

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
Database FERET (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.

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

Difference With Previous Work Coding Scheme Compass Masks Face description Face recognition Conclusions

A novel local feature descriptor
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 具有识别性

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

LBP The method discards most of the information in the neighborhood.
It limits the accuracy of the method It makes the method very sensitive to noise Moreover, these drawbacks are more evident for bigger neighborhoods

Directional (LDiP) & Derivative (LDeP)
Miss some directional information (the responses’ sign) by treating all directions equally Sensitive to illumination changes and noise, as the bits in the code will flip and the code will represent a totally different characteristic 边缘响应 方向没有指明 计算的时候忽略了响应的符号问题

LDN LBP Key points of LDN Direction number Sign information gradient
The coding scheme is based on directional numbers, instead of bit strings, which encodes the information of the neighborhood in a more efficient way 基于方向码 名字来源 The implicit use of sign information, in comparison with previous directional and derivative methods we encode more information in less space, and, at the same time, discriminate more textures 隐含方向 The use of gradient information makes the method robust against illumination changes and noise 6-bit

LDN Key points of LDN Direction number Sign information gradient

Coding Scheme - + - + Direction number Sign information 同一张图像上 方向不同的区分
we pick the prominent information of each pixel’s neighborhood. Therefore, our method filters and gives more importance to the local information before coding it, while other methods weight the grouped (coded) information

Coding Scheme

Compass Masks 𝐿𝐷𝑁 𝐾 𝐿𝐷𝑁 𝜎 𝐺 Kirsch masks derivative-Gaussian mask
gradient information Compass Masks Two kinds of masks 𝐿𝐷𝑁 𝐾 Kirsch masks 𝐿𝐷𝑁 𝜎 𝐺 derivative-Gaussian mask

M3 M2 M1 M4 M0 M5 M6 M7 Compass Masks Kirsch masks North 5 -3 5 -3 -3
5 -3 -3 5 M3 M2 M1 North-West North- East 5 -3 M3 M2 M1 M4 M0 M5 M6 M7 M4 M0 -3 5 West East -3 5 -3 5 -3 5 M5 M6 M7 South- West South- East South

Therefore, use Gaussian smoothing to stabilize the code in presence of noise 受 Kirsch Mask的启发 Generate a compass mask,{M0σ,...,M7σ}, by rotating Mσ, 45°apart, in eight different directions

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
Database FERET (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.

Experiment Results Face recognition accuracy
small neighborhoods (3×3, 5×5, 7×7) medium neighborhoods (5×5, 7×7, 9×9) large neighborhoods (7×7, 9×9, 11×11) 没有预处理 LPQ GGPP 相位信息 phase information

Experiment Results Noise Evaluation With white Gaussian noise
GGPP Global Gabor Phase Pattern

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. 在特定情况下，使用不同大小的组合达到更好效果 经过作者的测试，LDN能经受表情、时间变化、光照变化等考验，在各种方法中表现较好

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