Bachelor's Project.pdf
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This document summarizes research on the Thatcher effect and its impact on face recognition. It begins with background on the Thatcher effect optical illusion and selecting the VGGFace2 dataset. It then describes two problems studied: 1) Detecting whether an image is "thatcherized" or normal using a neural network, achieving over 99% accuracy. 2) Assessing how thatcherized images impact face recognition training, finding that including more thatcherized images improves test accuracy. The document concludes future work could use generative models and other illusions to further understand robust face recognition.
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To find faces in an image, the image has to be in the black and white format as colour data
is not necessary.
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This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
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We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
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Laureate Online Education Internet and Multimedia Technolog.docx
Laureate Online Education Internet and Multimedia Technology
© All right reserved, 2000-2008. The Internet and Multimedia Technology module, in all its
parts: syllabus, guidelines, lectures, discussion questions, technical notes, images, labs,
projects and any additional material is copyrighted by Laureate Online Education B.V.
Module: MSC MD (INTMMT) Seminar 3
Bitmaps and Colour
Introduction
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experiment.
Bitmapped images have none of the complexity of 3D graphics and yet they are
the final output from a 3D system. Often the 2D image desired will first be
created in 3D and then, after the perspective transformation is applied and it
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screen may state it provides so many dots per inch as its resolution. This is a
measure of the manufacturing quality. Depending on the size of the screen we
will have a different number of dots per pixel for a given computer resolution. It is
also rather short sighted to keep an image at a small resolution since it will look
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Compression schemes are considered to be of two types; Lossy and Lossless.
With lossless compression we can always regain the original quality whereas
with lossy compression we sacrifice some aspect of quality depending on the
application. When dealing with multimedia and the Internet we are very
concerned to obtain the best compression ration for a given quality of service
(QoS). The compression ratio is given by;
Compression Ratio = Uncompressed file size
Compressed file size
Most compression schemes rely on taking advantage of redundancy in the data
(in our case, the stream of pixels which make up the image). This takes many
forms. If we examine one scan line of an image we will notice that many pixels
1
are the same colour so stating for example Red 100 times is wasteful. It is
obviously shorter to store the value for red and its length. This is the basic idea
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appreciate that we do not need the full range twenty four bits can provide. If we
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原版定制【微信:bwp0011】《(lincoln学位证书)英国林肯大学毕业证文凭学位证书》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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Any camera in the last 10 years, probably has face detection in action:
Face detection is a great feature for cameras. When the camera can automatically pick out
faces, it can make sure that all the faces are in focus before it takes the picture.
But this concept uses it for a different purpose — finding the areas of the image that has to be passed to the next step of the procedure.
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Load a training face image of a known person.
Load another picture of the same known person.
Load a picture of a totally different person.
Then the algorithm looks at the measurements it is currently generating for each of those three images. It then tweaks the neural network slightly so that it makes sure the measurements it generates for #1 and #2 are slightly closer while making sure the measurements for #2 and #3 are slightly further apart.
After repeating this step millions of times for millions of images of thousands of different people, the neural network learns to reliably generate 128 measurements for each person. Any ten different pictures of the same person should give roughly the same measurements.
Machine learning people call the 128 measurements of each face an embedding. The idea of reducing complicated raw data like a picture into a list of computer-generated numbers comes up a lot in machine learning.
This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
After doing the comparative analysis by processing all the known images, our software finally tells the result of test image by displaying its name.
We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
When, we isolated the faces in our image. But now we have to deal with the problem that faces turned different directions look totally different to a computer:
To account for this, we will try to warp each picture so that the eyes and lips are always in the sample place in the image. To do this, we are going to use an algorithm called face landmark estimation .
The basic idea is we will come up with 68 specific points (called landmarks) that exist on every face — the top of the chin, the outside edge of each eye, the inner edge of each eyebrow, etc. Then we will train a machine learning algorithm to be able to find these 68 specific points on any face.
It has successfully run the alg
Report face recognition : ArganRecogn
The goal of this report is the presentation of our biometry and security course’s project: Face recognition for Labeled Faces in the Wild dataset using Convolutional Neural Network technology with Graphlab Framework.
Digital Future of the Surgery: Brining the Innovation of Digital Technology i...
Sungkyunkwan University's Department of Human ICT Convergence discusses how digital technology such as wearable devices, augmented reality, artificial intelligence, and 3D printing can be integrated into surgery. Wearable devices like Google Glass have been used in operating rooms to view CT scans and consult with distant colleagues. Augmented reality overlays 3D models onto live views to help identify tumors. Artificial intelligence like IBM Watson assists with treatment suggestions by analyzing medical literature and cases. The digital future aims to connect experts globally and enhance surgical training, planning and guidance.
Face and Eye Detection Varying Scenarios With Haar Classifier_2015
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Laureate Online Education Internet and Multimedia Technolog.docx
Laureate Online Education Internet and Multimedia Technology
© All right reserved, 2000-2008. The Internet and Multimedia Technology module, in all its
parts: syllabus, guidelines, lectures, discussion questions, technical notes, images, labs,
projects and any additional material is copyrighted by Laureate Online Education B.V.
Module: MSC MD (INTMMT) Seminar 3
Bitmaps and Colour
Introduction
We will turn our attention to Bitmaps and Colour during this Seminar. As we did
in Seminar 2, we will again recommend some applications and encourage you to
experiment.
Bitmapped images have none of the complexity of 3D graphics and yet they are
the final output from a 3D system. Often the 2D image desired will first be
created in 3D and then, after the perspective transformation is applied and it
becomes 2D, modified and enhanced. Let us take a look at some ideas
associated with Bitmaps.
We start with the resolution or measure of the granularity of the image. It is a
common error to assume that an image having 640 by 480 pixels on the screen
cover s only that number of phosphor dot triads on the face of the monitor. A
screen may state it provides so many dots per inch as its resolution. This is a
measure of the manufacturing quality. Depending on the size of the screen we
will have a different number of dots per pixel for a given computer resolution. It is
also rather short sighted to keep an image at a small resolution since it will look
very ‘blocky’ if enlarged or viewed at a greater resolution than originally stored.
This gives rise to large files being kept just in case we need the resolution! One
obvious way around this is to compress the image.
Compression schemes are considered to be of two types; Lossy and Lossless.
With lossless compression we can always regain the original quality whereas
with lossy compression we sacrifice some aspect of quality depending on the
application. When dealing with multimedia and the Internet we are very
concerned to obtain the best compression ration for a given quality of service
(QoS). The compression ratio is given by;
Compression Ratio = Uncompressed file size
Compressed file size
Most compression schemes rely on taking advantage of redundancy in the data
(in our case, the stream of pixels which make up the image). This takes many
forms. If we examine one scan line of an image we will notice that many pixels
1
are the same colour so stating for example Red 100 times is wasteful. It is
obviously shorter to store the value for red and its length. This is the basic idea
behind Run Length Encoding (RLE). A simple compression related to colour is to
appreciate that we do not need the full range twenty four bits can provide. If we
sample the image for the 256 commonest shades then we can reduce the
storage for colour by two thirds by sacrificing fine shading differences. A further
approach is useful with video and makes use of the ...
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Face detection and tracking in a video sequence
A novel approach performed to detect and track face in video sequence by combining 2 different algorithms and is tested across the existing algorithm of same category.
Face Detection And Tracking
Aim of uploading this document is to help all the student those who want to learn how to use video tool box of matlab in solving real life problem.
A COMPREHENSIVE STUDY ON OCCLUSION INVARIANT FACE RECOGNITION UNDER FACE MASK...
The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.
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一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Bachelor's Project.pdf
- 1. THATCHER EFFECT AND FACE RECOGNITION By: Erfan Alimohammadi Supervisor: Prof. Ebrahimi Moghaddam Date: Summer 2020
- 2. MOTIVATION AND BACKGROUND WHAT IS THATCHER EFFECT? DATASET THE FIRST PROBLEM THE SECOND PROBLEM CONCLUSION REFERENCES TABLE OF CONTENTS 01 02 03 04 05 06 07
- 3. MOTIVATION AND BACKGROUND 01 What does motivate us to solve these kind of problems?
- 4. MOTIVATION AND BACKGROUND How do computers see optical illusions? Do they see them in the way we see them? These horizontal lines are parallel. There is only one color in the vertical rectangle.
- 5. WHAT IS THATCHER EFFECT? It’s an optical illusion! 02
- 6. WHAT IS THATCHER EFFECT? You’re given a face image. 1. Flip the left eye vertically. 2. Flip the right eye vertically. 3. Flip the mouth vertically. 4. Flip whole image vertically. Now, you have a “thatcherized” image which causes an optical illusion. You’ll notice it when you turn the image upside-down. Turn your phone upside-down A thatcherized image The same image, but upside-down
- 7. DATASET What are some popular face datasets and which one should we use for solving our problems? 03
- 8. DATASET ● LFW ● CASIA WebFace ● CelebA ● MS-CELEB-1M ● VGGFace2 (2GB): 500 identities ● VGGFace2 (40GB): 9000 identities After selecting VGGFace2 among these well-known datasets, I decided to crop its images using MTCNN face detection algorithm.
- 9. THATCHERIZED DATASET Detecting position of the eyes and the mouth, has helped me to automatically thatcherize each image of my original dataset separately. dlib library 68 facial landmarks detection
- 10. THE FIRST PROBLEM 04 Is this image thatcherized, or not?
- 11. There are two normal faces and two thatcherized faces here. You’ll only notice it when you flip all four images vertically. WHICH ONES ARE THATCHERIZED? WHICH ONES ARE NORMAL?
- 12. APPROACH TO THE FIRST PROBLEM I have used a neural network for solving this binary classification problem: 1. 5 Convolutional layers with ReLU 2. Maxpooling layer 3. Dropout 4. 4 Fully-connected layers with ReLU 5. Apply sigmoid on the output And, the used loss function was binary cross entropy.
- 13. training accuracy 0.994 test accuracy 0.999 RESULT ON VGGFACE2 DATASET (2GB)
- 14. training accuracy 0.999 test accuracy 0.999 RESULT ON VGGFACE2 DATASET (40GB)
- 15. THE SECOND PROBLEM Effects of Thatcher illusion on face recognition 05
- 16. APPROACH TO THE SECOND PROBLEM Inception ResNet v1 architecture has been used here with cross entropy loss function. Inception ResNet v1 structure
- 17. training set thatcherized images share 0% 10% 20% training accuracy 0.910 0.955 0.978 test accuracy 0.820 0.945 0.968 RESULT ON VGGFACE2 (2GB)
- 18. training set thatcherized images share 0% training accuracy 0.890 test accuracy 0.735 RESULT ON VGGFACE2 DATASET (40GB)
- 20. CONCLUSION AND FUTURE WORKS ● Training on thatcherized images increases our network robustness in face recognition problems. ● Generative networks can be used for creating thatcherized images. ● We may use Grad-CAM for more visualizations. ● We can test other optical illusions too. ● Future researches can be continued in the field of cognitive sciences.
- 21. REFERENCES 07 Where do images, datasets, libraries, etc. come from?
- 22. REFERENCES ● Slide images from Wikipedia and CelebA dataset ● Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. VGGFace2: A dataset for recognising face across pose and age, International Conference on Automatic Face and Gesture Recognition, 2018 ● K. Zhang, Z. Zhang, Z. Li and Y. Qiao. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks, IEEE Signal Processing Letters, 2016 ● V. Kazemi, J. Sullivan: One millisecond face alignment with an ensemble of regression trees, 2014 ● Tim Esler’s FaceNet PyTorch repository: https://github.com/timesler/facenet-pytorch
- 23. THE END! With special thanks to: Prof. Mohsen Ebrahimi Moghaddam Mr. Ali Nazari