...REVIEWS, REFINEMENTS AND NEW IDEAS IN FACE RECOGNITION Edited by Peter M. Corcoran Reviews, Refinements and New Ideas in Face Recognition Edited by Peter M. Corcoran Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Mirna Cvijic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright hfng, 2010. Used under license from Shutterstock.com First published July, 2011 Printed in Croatia A free online edition of this book is available...
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...Intel® Technology Journal | Volume 18, Issue 4, 2014 HETERogEnEoUs FAcE REcognITIon: An EmERgIng TopIc In BIomETRIcs Contributor Guodong Guo West Virginia University An emerging topic in biometrics is matching between heterogeneous image modalities, called heterogeneous face recognition (HFR). This emerging topic is motivated by the advances in sensor technology development that make it possible to acquire face images from diverse imaging sensors, such as the near infrared (NIR), thermal infrared (IR), and three-dimensional (3D) depth cameras. It is also motivated by the demand from real applications. For example, when a subject’s face can only be acquired at night, the NIR or IR imaging might be the only modality for acquiring a useful face image of the subject. Another example is that no imaging system was available to capture the face image of a suspect during a criminal act. In this case a forensic sketch, drawn by a police artist based on a verbal description provided by a witness or the victim, is likely to be the only available source of a face of the suspect. Using the sketch to search a large database of mug-shot face photos is also a heterogeneous face recognition problem. Thus it is interesting to study the HFR as a relatively new topic in biometrics. In this article, several specific HFR problems are presented, and various approaches are described to address the heterogeneous face matching problems. Some future research directions are discussed as well to advance...
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...Abstract— Face recognition is a biometric system used to verify or identify a person from a digital image. This involve extracting features of face and then recognize it, regardless of expression, lighting, illumination, transformations (translate, rotate and scale image), ageing and pose. The existing approaches are deblurring based, joint deblurring and recognition, deriving blur invariant features and direct approach, which uses convolution model for performing face recognition in the presence of blur. So these methods cannot handle non uniform blurring situations that frequently arise from rotations and tilts in hand held cameras. In this paper, face recognition is done, in the presence of space varying motion blur. We have taken the concept that the set of all images obtained by non-uniformly blurring a given image form a convex set. We develop an algorithm based on TSF (Transformation Spread Function) model. On each focused...
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...Re Face Recognition Paper Adriana Zachry Psych/560 November 13, 2012 Christopher Wessinger Face Recognition Paper Face recognition develops slowly through life. Recognizing a face can be a difficult for the individual and also for the brain system that processes. The complexity of recognizing individual faces can be a difficult task at times. Recognizing faces also includes looking at an individual’s emotional expression and then, being able to take that information and processing it. This can be more complicated because facial recognition also includes the processing of emotions and emotional content. The brain can easily recognize a face without encountering any complications. Facial identification is essential for recognition of people in the social context within our society. The basic process of visual perception includes translating incoming stimulus into a perception and memory. When an individual will initially sees an object or a person, this information then gets processed through the brain. Bottom up and top down processing plays a critical role in object recognition. When we first look at an object we process it. This is called bottom up processing. When people apply previous knowledge to that object, it is known as top down processes. There is also a process when we recognize an object; we match an incoming object with stored information that helps us to recognize what is before us. A study was conducted by Palmer, Rsich and Chase on the perspective...
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...Journal of Computer Science 8 (9): 1585-1593, 2012 ISSN 1549-3636 © 2012 Science Publications Human Face Detection and Recognition using Web-Cam Petcharat Pattanasethanon and Charuay Savithi Depatment of Business Computer, Faculty of Accountancy and Management, Mahasarakham UniversityKamreang, Kantharawichai, Mahasarakham 44150, Thailand Abstract: Problem statement: The illuminance insensitivity that reflects the angle of human facial aspects occurs once the distance between the object and the camera is too different such as animated images. This has been a problem for facial recognition system for decades. Approach: For this reason, our study represents a novel technique for facial recognition through the implementation of Successes Mean Quantization Transform and Spare Network of Winnow with the assistance of Eigenface computation. After having limited the frame of the input image or images from Web-Cam, the image is cropped into an oval or eclipse shape. Then the image is transformed into greyscale color and is normalized in order to reduce color complexities. We also focus on the special characteristics of human facial aspects such as nostril areas and oral areas. After every essential aspectsarescrutinized, the input image goes through the recognition system for facial identification. In some cases where the input image from the Web-Cam does not exist in the database, the user will be notified for the error handled. However, in cases where the image exists...
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...Thesis Paper Outline Format I. Introduction: In this section, give the reader an idea of why your paper will be important and/or interesting, what you will be arguing, and make the organization of the paper clear to the reader. a. Explanation of purpose and background information (optional): Explain why this topic needs to be written about (may require some background on the topic) b. Thesis statement: A basic statement of your position; your answer to your research question c. Expanded thesis statement: A brief listing of the major points that you will make in your paper, in the order in which you will make them II. Arguments: Each of your main arguments can either argue a point that supports your position, or argue against something you believe is wrong. This is a lengthy paper, so ideally you will have more than three arguments to make. You should make as many as you can. Organize your arguments to flow from one to the next or, ideally, to put your strongest arguments first and last. a. Argument 1 i. Supporting evidence (author, pg. or para. #) ii. More supporting evidence! (author, pg. or para. #) iii. Even more supporting evidence!! (author, pg. or para. #) b. Argument 2 i. Supporting evidence (author, pg. or para. #) ii. More supporting evidence! (author, pg. or para. #) iii. Even more supporting evidence!! (author, pg. or para. #) c. Argument 3 i. Supporting evidence (author, pg. or para. #) ii. More supporting evidence! (author, pg. or para...
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...Other-Race Effect in Face Recognition As humans, we come into contact with many faces in a day. The capability of these homosapiens to precisely distinguish thousands of faces is incredible seeing that all faces have approximately the similar arrangement. Nevertheless, this “gift” does not spread similarly the same to all faces. Sporer (2001) stated that humans commonly exhibit weaker remembrance for faces of another race compared to own-race faces (as cited in Hancock and Rhodes, 2008). The majority of us must have heard this line, “How am I to know if I have ever seen the person previously? They all look the same to me.” When we hear an individual say this, we’re prone to assume that the individual is racist, but is it possible that there could be a particular theory behind the notion? This occurrence is identified as the Other-Race Effect (ORE) (ORE). Tanaka, Kiefer and Bukach (2004) mentioned that the Other-Race Effect (ORE) states that individuals have a higher probability to recall and identify faces of people who are from their own race rather instead of their own racial group. Extensive evidence has proven that adults are better at distinguishing faces of their own race than those of unfamiliar races (Meissner and Brigham, 2001). The Other-Race Effect (ORE) are not stemmed from the intrinsic variances in the discriminability of diverse populations of faces. Instead, it is due to the different approaches people process own and other-race faces (Rhodes et al., 2009)...
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...專題製作成果報告 專題研究計畫名稱 近紅外線影像人臉辨識 組員： 姓名: 黃偉倫 學號: B9921243 姓名: 林稟軒 學號: B9921230 指導教師: 謝堯洋教授 中華民國102年6月28日 摘要 人臉辨識系統的應用非常的廣泛，人臉辨識的技術在這幾十年來也已經有不少的研究成果，因此有不少增加辨識率的方法被提出。但要如何處理當姿勢角度改變、表情改變、照片裡人臉的大小不一，甚至光所造成的亮度問題所產生的影像變化量，這是人臉辨識的一大難題。我們將針對目前提出的眾多增加辨識率之方法進行文獻探討，藉此分析比較各種人臉辨識方法之優缺點，以提供未來進行相關研究時的依據與參考，以有效增加人臉的辨識率。 而我們所做的的方面是在於紅外線的人臉辨識，而人臉辨識的步驟通常都是先把照片做處理，才會進行拿去進行辨識的工作。在紅外線人臉識別裡，有許許多多人做過了很多種方式去辨識，我們看了其中的6種辨識法來研讀，並且挑了其中的PCA、LDA、SVM來實際製作跟測試。 1. Principal Component Analysis(PCA) 2. Linear Discriminant Analysis(LDA) 3. Independent Component Analysis (ICA) 4. Support Vector Machines (SVM) 5. ARENA (基於Memory-based) 6. Unified Bayesian Framework 目錄 摘要 i 目錄 ii 第一章、緒論 1 1-1 研究動機 1 1-2 研究目的 2 第二章、理論基礎 3 2-1 研究方法 3 2-1-1 Principal Component Analysis (PCA) 3 2-1-2 Linear Discriminant Analysis (LDA) 3 2-1-3 Independent Component Analysis (ICA) 3 2-1-4 Support Vector Machines (SVM) 3 2-1-5 ARENA (基於 Memory based) 4 2-1-6 Unified Bayesian Framework 4 2-2 臉部辨識的基本過程 4 2-3 MATLAB介紹 7 第三章、實作或模擬過程 8 3-1 建立人臉資料庫以及其內部樣式 8 3-2 資料庫取樣方法 9 3-3 PCA辨識 9 3-3-1 SVD在PCA上的應用 9 3-3-2 PCA訓練方法 10 3-3-3 PCA測試方法 11 3-4 LDA辨識 11 3-5 SVM辨識 12 3-5-1 SVM訓練方法 12 3-5-2 SVM測試方法 12 3-5-3 multi SVM 12 四、測試或模擬分析結果與討論 13 4-1 測試結果 13 4-1-1 測試PCA、LDA及SVM_Linear第一階段 13 4-1-2 測試PCA、LDA及SVM_Linear第二階段 14 4-1-3 SVM影像降維度與未降維度 15 4-1-4 基於PCA第一階段之線性SVM與多項式SVM 16 4-1-5 測試結果比較圖 18 4-2 測試討論...
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...TO: J Corporation FROM: Yizhen Gong RE: §357(c) gain IRS examination DATE: October 13, 2014 Facts Joe owns 100% of the stock of J Corporation. Joe plans on contributing a parcel of land to J co. having a fair market value of $1.5 million and a basis of $350,000. Further, the land is subject to a mortgage of $1.2 million. To avoid the gain that would result on this transaction, due to §357(c), Joe plans on contributing a promissory note to J Corporation of with a face value of $850,000. He claims that this note will have a basis equal to its face value thus eliminating the gain caused by §357(c). Issues Whether Joe’s transfer of a promissory note to its wholly owned corporation, in an amount equal to the excess of liabilities over the basis of assets contributed in a §351 transfer, avoids §357(c) gain recognition? Can he increase the basis of the assets by transferring his own promissory note to the corporation? Conclusion 1. Joe received stock valued at $300,000 and thus realized a gain of $1,150,000 ($300,000+$1,200,000-$350,000). §357(c) requires Joe recognize a gain of $850,000 ($1,200,000-$$350,000). 2. It depends on whether Joe’s note has a basis in Joe’s hands for purpose of section §357(c). Analysis 1. Under §357(c), no gain or loss is if property is transferred to a corporation solely in exchange for stock of that corporation if, immediately upon the transfer, the transferors are in control of the corporation. Joe transferred...
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...technology is finally able to handle the extensive amounts of equations going on to create the virtual neurons necessary to recognize images and or speech. Watch Video at this link: What is DeepFace? 'Human-Level' Face Matching, Explained https://www.youtube.com/watch?v=13FZHiXJSsE Application: Although the concept of facial recognition is not a breakthrough, the uses for it are. Facebook states that the DeepFace technology will be used to improve user privacy to dispel privacy concerns it has had in the past. DeepFace will be able to identify the user in a photo and notify them before allowing them to be tagged in it without their permission. This is especially useful as it will allow the user to blur out their face in images that may be embarrassing or even incriminating. This will notify uses whether the person uploading the photo is a friend or a stranger but only allows users to see the identities of the people they are already friends. Google and the government have taken a special interest in this application. Google has developed its own variation of this technology called FaceNet that boasts more than 86% accuracy at identifying whether images are the same person, while DeepFace has an accuracy score of 97% using the same recognition data set called Labeled Faces in the Wild. The difference between the FaceNet and...
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...* Degree of Alignment * Walgreens holds a reputation as the local trustworthy neighborhood convenient store, several which are accessible 24 hours a day, 7 days a week. This image is important to the face and ethics of the business and so we share a vision and strive to maintain its status and recognition. Walgreens wants to achieve customer satisfaction while providing everyday household items, necessities, and other merchandise. They intend to accomplish this shared vision by tending to customers ensuring satisfaction, keeping the atmosphere of the business neat and clean, and conveniently making the locations available internationally. The organization’s actual plans and actions also strive for this same goal, however, there are always other factors such as whether employees are being rude to customers, the maintenance of certain locations are not being kept, or if competition was rising causing us to lose business. * They offer services in various areas and product categories. Some of these categories include Pharmacy & Health; Photo; Beauty & Personal care; Medicine & Treatments; and Vitamins & Supplements. Other categories of interest at Walgreens include Sexual Wellness; Grocery; Baby, Kids & Toys; Household; Diet & Fitness; As Seen on TV; and seasonal products. Through providing these various products and services, they will achieve success in their goal of customer satisfaction and being the leading manufacturer for these services over...
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...As Nadine developes experiences in a work place she can also sharpen her skills of the task at hand. This makes Nadine a perfect AI for any work place because she will be able to adapt and even possibly assist others. The team of Nadine’s creators believe that people with autism or dementia would benefit from being around her. People who are completely alone at an old age will have a companion to talk to. B. “Sophia” * Created by Hanson Robotics at SXSW Interactive located in Austin, TX. * Two sophisticated cameras in both eyes, used to interact with humans * Tracks facial expressions and eye movement of people and also recognize them * Face made of rubbery material called “Frubber” to mimic elasticity of human face * Hanson uses a combination of Alphabet's Google Chrome voice recognition technology and other software that helps Sophia process speech, hold a conversation, remember...
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...Punch The Punch The most important thing to remember about leaving the past behind is to never look back, no matter how tempting it might be. That means leaving the physical and mental pain with the past. They never explained how much your fist hurts after that swing. The fact that the cheekbone of boy your age is as firm as the cement floors just a few metres away. This is the easy pain though. The pain which hurts most is when your throbbing fist becomes still and the adrenaline you felt is all but a memory. I tried to argue to the ref that he was being a grub all game, that I was provoked, that he threw the first punch and that some sort of justification would ease my pain. Constant vulgarities being tossed at me, elbows to the face, rubbing my face into my own home turf ground, tasting the bitterness of the mud and feeling the dirt seeping into my pores. There’s a limit to how many times you can tell someone to back off, before the testosterone within you breaks through your self-control barrier and forces you to make sudden decisions which affect your life. So I threw my punch. Then he threw his. Exchanging rapid punches like an eager child opening a birthday present. Constant yelling from the sidelines, in the background; whether it was abusive or positive comments. We didn’t hear the words, not in our current state. Those punches and jabs were far from swift though. Still half the time they scraped past our slippery shoulders. That didn’t die down the velocity of the ‘fight’...
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...ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS Fabian Timm and Erhardt Barth Institute for Neuro- and Bioinformatics, University of L¨ beck, Ratzeburger Allee 160, D-23538 L¨ beck, Germany u u Pattern Recognition Company GmbH, Innovations Campus L¨ beck, Maria-Goeppert-Strasse 1, D-23562 L¨ beck, Germany u u {timm, barth}@inb.uni-luebeck.de Keywords: Eye centre localisation, pupil and iris localisation, image gradients, feature extraction, shape analysis. Abstract: The estimation of the eye centres is used in several computer vision applications such as face recognition or eye tracking. Especially for the latter, systems that are remote and rely on available light have become very popular and several methods for accurate eye centre localisation have been proposed. Nevertheless, these methods often fail to accurately estimate the eye centres in difficult scenarios, e.g. low resolution, low contrast, or occlusions. We therefore propose an approach for accurate and robust eye centre localisation by using image gradients. We derive a simple objective function, which only consists of dot products. The maximum of this function corresponds to the location where most gradient vectors intersect and thus to the eye’s centre. Although simple, our method is invariant to changes in scale, pose, contrast and variations in illumination. We extensively evaluate our method on the very challenging BioID database for eye centre and iris localisation. Moreover...
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...Project – an Automated Make-up color selection system. Supervisor – Dr. H.L.Premarathne Field(s) of concern – Artificial Neural Networks, Fuzzy Logic, Image Processing, Data Classification, make-up Background: Women typically like to be in the centre of attraction of other the people. In order to be elegant looking and to get the attention of others, ladies often use make-up. Make-up is a favorite topic of women, and is a primary concern, not only when attending functions such as weddings, parties, but in day-to-day life when going for work too. The success of make-up relies on how well one can select the colors that matches her skin color, eye color, shape of the face and other relevant features. Make-up is also an art; hence one should have a good artistic eye to select the make-up which suits her. Inappropriate applying of make-up will cause a person to be in the centre of sarcasm and annoyance, instead of being in the centre of attraction. This is why; ladies often take the service of a beautician. A beautician is a professional who’s trained and who has expertise knowledge on beauty therapy and make-up. With experience, a beautician can match the make-up colors to suit a person, according to her appearance and personality. However, one does not need the help of a beautician, if that person can choose the appropriate make-up colors for herself. Introduction: Selection of colors for a make-up is vital for a Beautician as well as for any lady who rely on make-up...
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