Real-Time Secure System for Detection and Recognition the Face of Criminals

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1 Available Olie at Iteratioal Joural of Computer Sciece ad Mobile Computig A Mothly Joural of Computer Sciece ad Iformatio Techology IJCSMC, Vol. 4, Issue. 9, September 2015, pg RESEARCH ARTICLE ISSN X Real-Time Secure System for Detectio ad Recogitio the Face of Crimials Dr. Khaldu.I.Arif 1, Dhafer.G.HONI 2 Computer Sciece Departmet, College of Educatio For Pure Sciece, Uiversity, Thi-Qar, Iraq khaldu.i.a.2014@gmail.com; daferkiy1991@gmail.com Abstract i this paper We preset a system for real-time, saved images Ad Video Recorded i this three Method We Ca detectig ad recogizig faces of a crimial at public place Or Checkpoit such that the system easy to lear Ad to implemetatio,the three method that used i the system make recogitio very accuracy, where if we ruig it will appear Logi widow i this scree should isert (userame Ad Password) to eter i the mai widow, i the mai widow we must firstly isert images to Database ad The choose the way that wat to recogitio, if the system recogitio the Face, the system will show the ame of the crimial ad i the same time the system will appear Dager Soud Refer that the Crimials Recogitio ad Matched i the Database, Besides that we ca make Easy update(reame,delete) to the Database without complexed. Keywords Priciple Compoet Aalysis, Eigefaces, Haar Features, Cascade Classifier 1. Itroductio Face recogitio is oe of the most active ad widely used techique [1][2] because of its reliability ad accuracy i the process of recogizig ad verifyig a perso s idetity. The eed is becomig importat sice people are gettig aware of security ad privacy. For the Researchers Face Recogitio is amog the tedious work. It is all because the huma face is very robust i ature; i fact, a perso s face ca chage very much durig short periods of time (from oe day to aother) ad because of log periods of time (a differece of moths or years). Oe problem of face recogitio is the fact that differet faces could seem very similar; therefore, a discrimiatio task is eeded. O the other had, whe we aalyze the same face, may characteristics may have chaged. These chages might be because of chages i the differet parameters. The parameters are: illumiatio, variability i facial expressios, the presece of accessories (glasses, beards, etc.); poses, age, fially backgroud. We ca divide face recogitio [3][4] techiques ito two big groups, the applicatios that required face idetificatio ad the oes that eed face verificatio. The differece is that the first oe uses a face to match with other oe o a database; o the other had, the verificatio techique tries to verify a huma face from a give sample of that face. Face recogitio has bee a active research area over the last 30 years. It has bee studied by scietists from differet areas of psychophysical scieces ad those from differet areas of computer scieces. Psychologists ad euroscietists maily deal with the huma perceptio part of the topic, whereas egieers studyig o machie recogitio of huma faces deal with the 2015, IJCSMC All Rights Reserved 58

2 computatioal aspects of face recogitio. Face recogitio has applicatios maily i the fields of biometrics, access cotrol, law eforcemet, ad security ad surveillace systems. 2. Biometric Measures Biometric systems are automated methods for idetifyig people through physiological or behavioral characteristics. Face recogitio As compared with other biometrics systems usig figerprit/palm-prit ad iris, face recogitio has distict advatages because of its ocotact process. Face images ca be(system would allow a perso to be idetified by walkig i frot of a camera ad captured from a distace without touchig the perso beig idetified, ad the idetificatio does ot require iteractig with the perso. I additio, face recogitio serves the crime deterret purpose because face images that have bee recorded ad archived ca later help idetify a perso. 3. Real-time image processig The term real-time imagig refers to real-time image processig ad image aalysis. Simply put, the camera gets images which are digitized ito computer graphics. Picture processig is ay form of sigal processig for which the iput is a image, such as a photograph or a video frame; the output of image processig may be either a image or a set of characteristics or parameters related to the image. Most image-processig techiques ivolve treatig the image as a two-dimesioal sigal ad applyig stadard sigal processig techiques to it. For this process the OpeCV library is used, which is able to query ad maipulate images straight from a camera coected to the computer [5]. 4. Face Detectio Face detectio is a computer visio techology that determies the locatios ad sizes of huma faces i arbitrary (digital) images. It detects facial features ad igores aythig else, such as buildigs, trees ad bodies. Face detectio ca be regarded as a specific case of object-class detectio. I object-class detectio, the task is to fid the locatios ad sizes of all objects i a digital image that belog to a give class. Examples iclude upper torsos, pedestrias, ad cars. huma face localizatio ad detectio is ofte the first step i applicatios such as video surveillace, huma computer iterface, face recogitio ad image database maagemet. Locatig ad trackig huma faces is a prerequisite for face recogitio ad/or facial expressios aalysis, although it is ofte assumed that a ormalized face image is available [6]. (a) real image (b)yale image Database Figure 1: Face Detectio 5. Face Detectio Techiques It s always good to kow that the method you're applyig is i fact the better oe amog all the rest. For that you should kow what methods have bee developed, their pros ad cos ad why you're ot usig those 2015, IJCSMC All Rights Reserved 59

3 istead, who kows you might fid some other method more useful for your project? This way you ca create your work with maximum efficiecy. kowig the curret work doe allows us to explore future possibilities as well. here are may ways to detect a face i a scee - easier ad harder oes [7]. 5.1 Fidig Faces i images with cotrolled backgroud The method that ca be used whe you simply have a frotal face image agaist a plai backgroud. I this case the software ca easily remove the backgroud, leavig face boudaries. If software uses this approach it teds to have a umber of differet classifiers for detectig differet types of frot-o faces, alog with some for profile faces. It will attempt to detect eyes, a ose, a mouth, ad i some cases eve a whole body for pictures that show more tha just a face [7] [8]. 5.2 Fidig faces by color The method that ca use to look for faces. It obviously requires that the photos or video images used be color. The software scas the picture lookig for areas that are of a typical ski color, the lookig for face segmets. A problem with this techique is that ski color varies from race to race, ad this method does ot work as well for all ski colors. Varyig lightig coditios chages the exact hue of a perso s ski i the picture, ad that ca have a major effect o facial detectio The advatage: If you have access to color images, you might use the typical ski color to fid face segmets Ad the disadvatage: It does't work with all kid of ski colors, ad is ot very robust uder varyig lightig coditios [9]. 5.3 Fidig faces by motio The face i the image is usually i motio So Whe you are usig video images you ca use movemet as a guide. Faces are usually movig i real-time videos, so oe optio is for the software to capture the movig area. Of course, other parts of videos also move, so the software eeds to look for particular referece poits to idicate that it is actually a face that is movig. Oe specific face movemet is blikig. If the software ca determie a regular blikig patter (two eyes blikig together, symmetrically positioed) the this is a good idicatio that there is a face. From this regular blikig patter the computer ca determie the area of the video image that is actually the face, usig oe of a umber of face models. There will be a umber of face models i the software, cotaiig the appearace, shape ad motio of faces. There are actually a variety of differet face shapes, roughly categorized as oval, rectagle, roud, square, heart ad triagle. As well as blikig, there are various other motios that sigpost to the computer that the image may cotai a face. These iclude raised eyebrows, flared ostrils, wrikled foreheads ad opeed mouths. Oce oe of these actios is detected, the computer will pass their face models over the video image ad try ad determie a facial match. Oce a face is detected, ad a particular face model matched with a particular movemet, the model is laid over the face, eablig face trackig to pick up further face movemets [10]. 5.4 Model-based Face Trackig A Face model ca cotai the appearace, shape, ad motio of faces. This techique uses the face model to fid the face i the image. Some of the models ca be rectagle, roud, square, heart, ad triagle. It gives high level of accuracy if used with some other techiques [11]. 6. Facial Feature Extractio I may problem domais combiig more tha oe techique with ay other techique(s) ofte results i improvemet of the performace. Boostig is oe of such techique used to icrease the performace result. Facial features are very importat i face recogitio. Facial features ca be of differet types: regio, key poit (ladmark), ad cotour. I this Method, AdaBoost: Boostig algorithm with Haar Cascade Classifier for face detectio ad fast PCA ad PCA with LDA for the purpose of face recogitio. All these algorithms are explaied oe by oe [10]. 2015, IJCSMC All Rights Reserved 60

4 7. The Viola-Joes Face Detector If oe were asked to ame a sigle face detectio algorithm that has the most impact i the 2000 s, it will most likely be the semial work by Viola ad Joes (2001). The Viola-Joes face detector cotais three mai ideas that make it possible to build a successful face detector that ca ru i real time: the itegral image, classifier learig with AdaBoost, ad the attetioal cascade structure [12]. 7.1 Weak classifier cascades The face detectio algorithm That used I my Thesis proposed by Viola ad Joes is used as the basis of our desig. The face detectio algorithm looks for specific Haar features of a huma face. Whe oe of these features is foud, the algorithm allows the face cadidate to pass to the ext stage of detectio. A face cadidate is a rectagular sectio of the origial image called a sub-widow. Geerally this sub-widow has a fixed size (typically pixels). This sub-widow is ofte scaled i order to obtai a variety of differet size faces. The algorithm scas the etire image with this widow ad deotes each respective sectio a face cadidate. The algorithm uses a itegral image i order to process Haar features of a face cadidate i costat time. It uses a cascade of stages which is used to elimiate o-face cadidates quickly. Each stage cosists of may differet Haar features. Each feature is classified by a Haar feature classifier. The Haar feature classifiers geerate a output which ca the be provided to the stage comparator. The stage comparator sums the outputs of the Haar feature classifiers ad compares this value with a stage threshold to determie if the stage should be passed. If all stages are passed the face cadidate is cocluded to be a face. So This Method is distiguished by three key cotributios. The first is the itroductio of a ew image represetatio called the Itegral Image which allows the features used by our detector to be computed very quickly. The secod is a learig algorithm, based o AdaBoost, which selects a small umber of critical visual features from a larger set ad yields extremely efficiet classifiers. The third cotributio is a method for combiig icreasigly more complex classifiers i a cascade which allows backgroud regios of the image to be quickly discarded while spedig more computatio o promisig object-like regios. The cascade ca be viewed as a object specific focus-ofattetio mechaism which ulike previous approaches provides statistical guaratees that discarded regios are ulikely to cotai the object of iterest. I the domai of face detectio the system yields detectio rates comparable to the best previous systems. Used i real-time applicatios, the detector rus at 15 frames per secod without resortig to image differecig or ski color detectio [10][12]. 2015, IJCSMC All Rights Reserved 61

5 7.2 Itegral Image Rectagle features ca be computed very rapidly usig a itermediate represetatio for the image which we call the itegral image. The itegral image at locatio x, y cotais the sum of the pixels above ad to the left of x, y so The itegral image is defied as the summatio of the pixel values of the origial image. The value at ay locatio (x, y) of the itegral image is the sum of the images pixels above ad to the left of locatio (x, y)., iclusive: where ii(x, y) is the itegral image ad i(x, y) is the origial image. Usig the followig pair of recurreces: s(x, y) = s(x, y 1) + i (x, y).... (1) ii (x, y) = ii (x 1, y) + s(x, y) (2) where s(x, y) is the cumulative row sum,s(x, 1) =0, ii ( 1, y) = 0 the itegral image ca be computed i oe pass over the origial image. Usig the itegral image ay rectagular sum ca be computed i four array refereces Clearly the differece betwee two rectagular sums ca be computed i eight refereces. Sice the two-rectagle features defied above ivolve adjacet rectagular sums they ca be computed i six array refereces, eight i the case of the three-rectagle features, ad ie for four-rectagle features [10][13][14]. Figure 3 : The value of the itegral image at poit (x, y) is the sum of all the pixels above ad to the left. Figure 4 : diagram Represet the Calculate of Itegral Image 2015, IJCSMC All Rights Reserved 62

6 7.3 Haar Features The face detectio procedure classifies images based o the value of simple features. There are may motivatios for usig features rather tha the pixels directly. The most commo reaso is that features ca act to ecode ad-hoc domai kowledge that is difficult to lear usig a fiite quatity of traiig data. For this system there is also a secod critical motivatio for features: the feature-based system operates much faster tha a pixelbased system. The simple features used are remiiscet of Haar basis fuctios. More specifically, we use three kids of features. The value of a two-rectagle feature is the differece betwee the sum of the pixels withi two rectagular regios. The regios have the same size ad shape ad are horizotally or vertically adjacet A three-rectagle feature computes the sum withi two outside rectagles subtracted from the sum i a ceter rectagle. Fially a four-rectagle feature computes the differece betwee diagoal pairs of rectagles. Give that the base resolutio of the detector is 24 24, the exhaustive set of rectagle features is quite large, 160,000. Note that ulike the Haar basis, the set of rectagle features is over complete. The value of a Haar-like feature is the differece betwee the sum of the pixel gray level values withi the black ad white rectagular regios [14]. Figure 5: Example rectagle features show relative to the eclosig detectio widow. The sum of the pixels which lie withi the white rectagles are subtracted from the sum of pixels i the grey rectagles. Two- rectagle features are show i (A) ad (B). Figure (C) shows a three-rectagle feature, ad (D) a four-rectagle feature. 7.4 Cascade The Viola ad Joes face detectio algorithm elimiates face cadidates quickly usig a cascade of stages. The cascade elimiates cadidates by makig stricter requiremets i each stage with later stages beig much more difficult for a cadidate to pass. Cadidates exit the cascade if they pass all stages or fail ay stage. A face is detected if a cadidate passes all stages [15]. Figure 6 : Cascade of stages. Cadidate must pass all stages i the cascade to be cocluded as a face. 2015, IJCSMC All Rights Reserved 63

7 Figure 7 : Explai Detail Of Cascaded Stages 7.5 Learig Results For the task of face detectio, the iitial rectagle features selected by AdaBoost are meaigful ad easily iterpreted. The first feature selected seems to focus o the property that the regio of the eyes is ofte darker tha the regio [16]. Figure 8 : The first ad secod features selected by AdaBoost. The two features are show i the top row ad the overlayed o a typical traiig face i the bottom row. The first feature compares the itesities i the mouth regios to the itesity i the ose ad the lower of the mouth bridge of the ose. 2015, IJCSMC All Rights Reserved 64

8 Figure 9: The Steps Of The AdaBoost algorithm for classifier learig Figure 10 : Weak learer costructor 2015, IJCSMC All Rights Reserved 65

9 Figure 11: The Fial Result Of Haar Cascade Classifier For Face Detectio 8. Face Recogitio Techiques Face recogitio is a techique to idetify a perso face from a still image or movig pictures with a give image database of face images. Face recogitio is biometric iformatio of a perso. However, face is subject to lots of chages ad is more sesitive to evirometal chages. Thus, the recogitio rate of the face is low tha the other biometric iformatio of a perso such as figerprit, voice, iris, ear, palm geometry, retia, etc. There are may methods for face recogitio ad to icrease the recogitio rate [10]. Stages of Facial recogitio systems i geeral: 1) Stage of the get the image (take) Acquire. 2) Detect stage that Extractig the facial image from the all image. 3) Aligmet ad Stadardizatio's stage (adjust the face agle with the camera) Alig agle. 4) Extract stage that will extract the importat feature from face. 5) The Matchig stage with the images that stored i DB. 6) Report stage that tell us if the face recogitio or Not. Face Detectio Face Extractio Face Recogitio Iput Image Recogitio Figure 12. Basic Block Flow Diagram of Face Recogitio. 2015, IJCSMC All Rights Reserved 66

10 Some of the basic commoly used face recogitio techiques are as below: Face Recogitio Approaches Aalytic methods Holistic methods Hybrid methods Geometry Template PCA Fisherfaces Modula View-based eigespaces Figure 13: Classificatio of face recogitio methods. 8.1 Geometry ad Templates(Aalytic) face recogitio were focused o detectig idividual features such as eyes, ears, head outlie ad mouth, ad measurig differet properties such as size, distace ad agles betwee features. This data was used to build models of faces ad made it possible to distiguish betwee differet idetities [16][17]. Advatages Robust agaist scalig, orietatio ad traslatio whe face is correctly ormalized Ca hadle high resolutio images efficietly Saves eighbourhood relatioships betwee pixels Ca hadle very low resolutio images Geometric relatios are stable uder varyig illumiatio coditios Good recogitio performace Drawbacks Sesitive to faulty ormalizatio Sesitive to oise ad occlusio Templates are sesitive to illumiatio Sesitive to perspective, viewig agle ad head rotatio (ca be improved usig more templates) Sesitive to facial expressios, glasses, facial hair. etc. Slow traiig ad recogitio/high computatioal complexity 8.2 Hybrid methods Hybrid face recogitio systems use a combiatio of both holistic ad feature extractio methods. [18][20] So I the field of patter recogitio, computer visio has emerged as a idepedet field of research activities. Withi this field face recogitio systems have especially tured out to be the focus of iterest for a wide field of demadig applicatios i areas such as security systems or idexig of large multimedia databases.holistic matchig ad Feature-based matchig approaches are the two major classes of face recogitio methods. Holistic matchig is based o iformatio theory cocepts; seeks a computatioal model that best describes a face, by extractig the most relevat iformatio cotaied i that face. Feature-based matchig is based o the extractio of the properties of idividual orgas located o a face such as eyes, ose ad mouth, as well as their relatioships with each other. Curret Face Recogitio methods, which are based o two-dimesio view of face images, ca obtai a good performace uder costrait eviromet. However, i the real applicatio face appearace varies due to differet illumiatio, pose, ad expressio. Each idividual classifier based o 2015, IJCSMC All Rights Reserved 67

11 differet appearace of face image has differet sesitivity to these variatios, which motivate to move towards hybrid approach [21][16][17]. 8.3 Holistic Methods (PCA & Fisherface) Holistic methods that do ot deped o detailed geometry of the faces. The mai difficulty i recogizig faces by feature-based approach is that it is ot easy to desig proper face models due to large variability of face appearace, e.g. pose ad light variace. Rather tha costructig face models based o geometry, a face recogitio system should lear the models automatically from a collectio of traiig images, that is to lear what attributes of appearace will be the most effective i recogitio. Of course, eough traiig data should be available i order the system to accout for variatios i images. These methods use global represetatio of faces by treatig images as a vector of gray level itesities. Hece, this approach ca be referred as image or appearace-based [19][17]. Advatages Robust agaist oise ad occlusio Robust agaist illumiatio, scalig, orietatio ad traslatio whe face is correctly ormalized Robust agaist facial expressios, glasses, facial hair, etc. Ca hadle high resolutio images efficietly Ca hadle very low resolutio images Ca hadle small traiig sets Fast recogitio/low Priciple computatioal Compoet cost Aalysis (PCA) Drawbacks Removes eighborhood relatioships betwee pixels Sesitive to faulty ormalizatio Sesitive to perspective, viewig agle ad head rotatio (ca be improved usig eige light-fields or other viewbased methods) Sesitive to large variatio i illumiatio ad strog facial expressios Slow traiig/high computatioal cost (with large databases) Priciple Compoet Aalysis (PCA) commo method based o holistic approach for face recogitio ad it is a dimesioality reductio techique that is used for image recogitio ad compressio. It is also kow as Karhue-Loeve trasformatio (KLT) or eigespace projectio. The mai goal of PCA is dimesioality reductio. Therefore, the eigevectors of the covariace should be foud i order to reach the solutio. The eigevectors correspod to the directios of the priciple compoets of the origial data, ad their statistical sigificace is give by their correspodig eigevalues.the first itroductio of a low-dimesioal characterizatio of faces was developed at Brow Uiversity. This was later exteded to eigespace projectio for face recogitio. recetly used eigespace projectio to idetify objects usig a lookup table to view objects at differet agles. The Method exteded grayscale eigefaces to color images. So PCA allows us to compute a liear trasformatio that maps data from a high dimesioal space to a lower dimesioal sub-space [22][23]. 2015, IJCSMC All Rights Reserved 68

12 Start Traiig Face E=Eigeface(Traiig Set) W=Weights(E, Traiig Set) Iput Ukow Image X Wx = Weight(E,X) D = avg(distace(w,wx)) D < X is a face X is a ot a face Figure 14: Architecture of PCA algorithm The PCA approach for face recogitio ivolves the followig iitializatio operatios: 1. Acquire a set of traiig images. 2. Calculate the eigefaces from the traiig set, keepig oly the best M images with the highest eigevalues. These M images defie the face space. As ew faces are experieced, the eigefaces ca be updated. 3. Calculate the correspodig distributio i M-dimesioal weight space for each kow idividual (traiig image), by projectig their face images oto the face space. Havig iitialized the system, the followig steps are used to recogize ew face images: 1. Give a image to be recogized, calculate a set of weights of the M eigefaces by projectig the it oto each of the eigefaces. 2. Determie if the image is a face at all by checkig to see if the image is sufficietly close to the face space. 3. If it is a face, classify the weight patter as eigher a kow perso or as ukow. 2015, IJCSMC All Rights Reserved 69

13 4. (Optioal) Update the eigefaces ad/or weight patters. 5. (Optioal) Calculate the characteristic weight patter of the ew face image, ad icorporate ito the kow faces The Statistical steps for algorithm PCA : Calculatig Eigefaces Step 1: Traiig Set Let a face image (x,y) be a two-dimesioal N by N array of itesity values. A image may also be cosidered as a vector of dimesio N 2, so that a typical image of size 256 by 256 becomes a vector of dimesio 65,536, or equivaletly, a poit i 65,536-dimesioal space. A esemble of images, the, maps to a collectio of poits i this huge space. Images of faces, beig similar i overall cofiguratio, will ot be radomly distributed i this huge image space ad thus ca be described by a relatively low dimesioal subspace. The mai idea of the pricipal compoet aalysis is to fid the vector that best accout for the distributio of face images withi the etire image space. These vectors defie the subspace of face images, which we call face space. Each vector is of legth N 2, describes a N by N image, ad is a liear combiatio of the origial face images. Because these vectors are the eigevectors of the covariace matrix correspodig to the origial face images, ad because they are face-like i appearace, they are referred to as eigefaces. Let the traiig set of face images be 1, 2, 3,, M. Figure 15: A set of traiig images from Digital Camera. Step 2: Mea Face The average face of the set if defied by [25]. 1 M M , IJCSMC All Rights Reserved 70

14 Step 3: Mea Subtracted Image Figure 16 :Mea Image The you will fid the differece Φ betwee the iput image Γi ad the mea image Ψ. [26]. Figure 17: Mea Subtracted Image Step 4 : Differece Matrix A is N x M, A T is M x N. Thus C will be a N x N matrix Each of mea subtracted traiig image vectors are the appeded colum wise to form differece matrix [27]. A [ M ] 2015, IJCSMC All Rights Reserved 71

15 Step 5: Create the covariace matrix The covariace matrix C Create by multiplyig the mea subtracted matrix by its traspose C 1 M M 1 T AA T Figure 18. Calculate the Covariace Matrix (C) Step 6 : Eige Vectors & Eige Values : Traiig set with the average face. This set of very large vectors is the subject to pricipal compoet aalysis, which seeks a set of M orthoormal vectors,, which best describes the distributio of the data. The kth vector, k is chose such that is a maximum, subject to k 1 M M 1 T ( ) T 1, l k l k 0, otherwise k 2 The vectors k ad scalars k are the eigevectors ad eigevalues, respectively, of the covariace matrix C 1 M M 1 T AA T 2015, IJCSMC All Rights Reserved 72

16 A [ 2... ] 2 N by 2 2 N, ad determiig the N where the matrix 1 M. The matrix C, however, is eigevectors ad eigevalues is a itractable task for typical image sizes. A computatioally feasible method is eeded to fid these eigevectors. If the umber of data poits i the image space is less tha the dimesio of the space (M< N 2 ), there will be oly M-1, rather tha N 2, meaigful eigevectors (the remaiig eigevectors will have associated eigevalues of zero). Fortuately, we ca solve for the N 2 -dimesioal eigevectors i this case by first solvig for the eigevectors of ad M by M matrix e.g., solvig a 16 x 16 matrix rather tha a 16,384 x 16,384 matrix ad the takig appropriate liear combiatios of the face images. Cosider the eigevectors of A T A such that Premultiplyig both sides by A, we have A T A from which we see that by M matrix AA A are the eigevectors of T T L A A L, where m m T A A T C AA liear combiatios of the M traiig set face images to form the eigefaces M. Followig this aalysis, we costruct the M, ad fid the M eigevectors of L. These vectors determie kk A, 1,..., M k1 With this aalysis the calculatios are greatly reduced, from the order of the umber of pixels i the images (N 2 ) to the order of the umber of images i the traiig set (M). I practice, the traiig set of face images will be relatively small (M< N 2 ), ad the calculatios become quite maageable. The associated eigevalues allow us to rak the eigevectors accordig to their usefuless i characterizeig the variatio amog the images. : Figure 19. Calculate Co-variace matrix(l) of lower dimesioal 2015, IJCSMC All Rights Reserved 73

17 Figure 20. Eigevalue Ad Eigevectors Figure 20. Select The best K Of eigevectors 2015, IJCSMC All Rights Reserved 74

18 : Face Recogitio Usig Eigefaces The eigeface images calculated from the eigevectors of L spa a basis set with which to describe face images. As metioed before, the usefuless of eigevectors varies accordig their associated eigevalues. This suggests we pick up oly the most meaigful eigevectors ad igore the rest, i other words, the umber of basis fuctios is further reduced from M to M (M <M) ad the computatio is reduced as a cosequece. Experimets have show that the RMS pixel-by-pixel errors i represetig cropped versios of face images are about 2% with M=115 ad M =40 [11]. I practice, a smaller M is sufficiet for idetificatio, sice accurate recostructio of the image is ot a requiremet. I this framework, idetificatio becomes a patter recogitio task. The eigefaces spa a M 2 dimesioal subspace of the origial N image space. The M most sigificat eigevectors of the L matrix are chose as those with the largest associated eigevalues. A ew face image is trasformed ito its eigeface compoets (projected oto face space ) by a simple operatio ( ) for =1,,M. This describes a set of poit-by-poit image multiplicatios ad summatios. T [,,..., '] The weights form a vector 1 2 M that describes the cotributio of each eigeface i represetig the iput face image, treatig the eigefaces as a basis set for face images. The vector may the be used i a stadard patter recogitio algorithm to fid which of a umber of predefied face classes, if ay, best describes the face. The simplest method for determiig which face class provides the best descriptio of a iput face image is to fid the face class k that miimizes the Euclidia distace 2 k ( k k is a vector describig the kth face class. The face classes k where are calculated by averagig the results of the eigeface represetatio over a small umber of face images (as few as oe) of each idividual. A face is classified as ukow, ad optioally used to created a ew face class. Because creatig the vector of weights is equivalet to projectig the origial face image oto to lowdimesioal face space, may images (most of them lookig othig like a face) will project oto a give patter vector. This is ot a problem for the system, however, sice the distace betwee the image ad the face space is simply the squared distace betwee the mea-adjusted iput image ad f M ' i1 i i, its projectio oto face space: 2 f Thus there are four possibilities for a iput image ad its patter vector: (1) ear face space ad ear a face class; (2) ear face space but ot ear a kow face class; (3) distat from face space ad ear a face class; (4) distat from face space ad ot ear a kow face class. I the first case, a idividual is recogized ad idetified. I the secod case, a ukow idividual is preset. The last two cases idicate that the image is ot a face image. Case three typically shows up as a false positive i most recogitio systems; i this framework, however, the false recogitio may be detected because of the sigificat distace betwee the image ad the subspace of expected face images. ) , IJCSMC All Rights Reserved 75

19 Figure 21 Represet Each Face Image a Liear Combiatio of all K Eigevectors 2015, IJCSMC All Rights Reserved 76

20 Figure 22: The Fial Result Of Face Recogitio Usig PCA 9. Proposed The System The aim Of the system proposed is a real-time system ad the two other method are to make the system i high accuracy ad to make sure that the crimial is the same that i the Database, The recogitio i real time I used ( Logitech 720p webcam ) to Acquire the Face s from the real word, Firstly Stage should be isert Traiig images to DB i this stage we ca isert images to DB i the method (Video Live, Video Recorded, Saved image),secod Stage choose the Way Of the Recogitio, here is the heart of the system such that if the system Recogize three Evet will appear (The ame of the crimial up his face, Dager Soud make attetio to the perso that used system ad the rectagle color of face will covert from gree to Red), Third Stage we ca Update the Traiig images i Database (Delete, Reame).The System Used (Yale Database images, Real world images take i LG G3 mobile Phoe),The deal with the system is very easy because more of the butto i the mai widow ot able i same time that make the user kow the steps of the system. 2015, IJCSMC All Rights Reserved 77

21 Figure 23. Welcome Scree of the system Applicatio GUI. Figure 24. The Operatio i the Face Recogitio System Figure 25. The Logi Widow 2015, IJCSMC All Rights Reserved 78

22 Figure 26. The Mai Widow of The System I the Yale database there are 111 Images For 10 perso each perso 11 images i differet positio with differet emotios. The recogitio rate is very high ( 88-89%) For Saved images, the figure show the recogitio rate. Figure 27. Yale Database with 111 gray images 2015, IJCSMC All Rights Reserved 79

23 Recogitio Rate Dhafer.G.HONI et al, Iteratioal Joural of Computer Sciece ad Mobile Computig, Vol.4 Issue.9, September- 2015, pg Figure 28. Face Recogitio Face Recogitio For differet expressios ad coditios Expressios ad coditios Figure 29. Recogitio Rate For Saved Picture 2015, IJCSMC All Rights Reserved 80

24 Figure 30. Recogitio For Video live mode Ad Saved Image mode 10. CONCLUSIONS The system has bee tested o a wide variety of face images(real images ad Yale DataBase images), with may emotios ad may differet agles other tha frotal face image, whether the perso is authorized or ot. The system is givig very high accuracy. The system is capable of usig multi cameras as the capturig device simultaeously. Thus, the system is good ad efficiet i the field of security the police to match the crimials ad also ca use for geeral purpose like olie Autheticatio ad also i the orgaizatio to determie if the perso who etry belog to the orgaizatio or ot. 2015, IJCSMC All Rights Reserved 81

25 REFERENCES [1] Aupam shukla, joydip dhar, Chadra prakash, Itelliget biometricsystem usig PCA ad R-LDA, global cogress o itelliget systems,ieee computer society2009. [2] Liu Zuxiog, Zeg Lihui, Zhag Heg, Appearace-based SubspaceProjectio Techiques for Face Recogitio, Iteratioal Asia Symposium o Itelliget Iteractio, IEEE [3] N. SuitaDevi ad K. Hemachadra, 'Automatic Face Recogitio System usig Patter Recogitio Techiques: A Survey', Iteratioal Joural of Computer Applicatios, vol. 83, o. 5, pp , [4] James E. Fowler, Compressive-Projectio Pricipal Compoet Aalysis, IEEE trasactios o image processig, [5] Kehtaravaz ad M. Gamadia, Real-time image ad video processig. [Sa Rafael, Calif.]: Morga & Claypool Publishers, [6] S.KALAS, MAMATA. "REAL TIME FACE DETECTION AND TRACKING USING OPENCV." Iteratioal Joural of Soft Computig ad Artificial Itelligece Volume-2.Issue-1 (2014) [7] Katibeh, Zahra. Techiques for Real-time Multi-perso Face Trackig for Huma-robot Dialogue. Thesis. Swedish Blekige Istitute of Techology, [8] D. Sil, S. Basu ad M. Nasipuri, 'A Adaptive Fuzzy Techique for Real-Time Detectio of Multiple Faces agaist a Complex Backgroud', Iteratioal Joural of Computer Applicatios, vol. 1, o. 23, pp , [9] M. ski, Computer recogitio systems 2. Berli: Spriger, [10] Kumar, K. Susheel, Shitala Prasad, Vijay Bhaskar Semwal, ad R. C. Tripathi. "Real Time Face Recogitio Usig AdaBoost Improved Fast PCA Algorithm." Iteratioal Joural of Artificial Itelligece & Applicatios IJAIA 2.3 (2011). [11] S. Kachare ad S. Vaga, Fashio Accessories usig Virtual Mirror, Iteratioal Joural Of Egieerig Ad Computer Sciece, vol. 4, o. 4, pp , [12] Viola, Paul ad Michael Joes (2001). Rapid object detectio usig boosted cascade of simple features. I: Proceedigs IEEE Cof. o Computer Visio ad Patter Recogitio [13] Juda Dog Dog. "DESIGNING A VISUAL FRONT END IN AUDIO-VISUAL AUTOMATIC SPEECH RECOGNITION SYSTEM." Thesis. Califoria/Sa Luis Obispo,, [14] Zhag, Cha, ad Zhegyou Zhag. Boostig-based Face Detectio ad Adaptatio. Sa Rafael, CA: Morga & Claypool, [15] Cho, Juguk, Shaham Mirzaei, Jaso Oberg, ad Rya Kaster. "Fpga-based Face Detectio System Usig Haar Classifiers." Proceedig of the ACM/SIGDA Iteratioal Symposium o Field Programmable Gate Arrays - FPGA '09 (2009). [16] Cai, Hua, Yog Yag, Fuheg Qu, ad Jiafei Wu. "Face Detectio ad Locatio System Based o Software ad Hardware Co-desig."Joural of Multimedia JMM 9.5 (2014). [17] Nes, Atle. Hybrid Systems for Face Recogitio,. Thesis. Norwegia/ Uiversity of Sciece ad Techology, 16th of Jue (2003). [18] R. Patel ad S. B. Yagik, A Literature Survey o Face Recogitio Techiques,, Iteratioal Joural of Computer Treds ad Techology, vol. 5, o. 4, pp , [19] D. N. Parmar ad B. B. Mehta, Face Recogitio Methods Applicatios,, Computer Techology & Applicatios,, vol. 4, o. 1, pp , , IJCSMC All Rights Reserved 82

26 [20] S. N. Kakarwal, Hybrid Feature Extractio Techique for Face Recogitio,," Iteratioal Joural of Advaced Computer Sciece ad Applicatios ", vol. 3, o. 2, pp , [21] R. C. Tripathi, R EAL T IME F ACE R ECOGNITION U SING ADAB OOST I MPROVED F AST PCA A LGORITHM,, Iteratioal Joural of Artificial Itelligece & Applicatios, vol. 2, o. 3, [22] M. Abdullah, M. Wazza, ad S. Bo-saeed, Optimizig Face Recogitio usig PCA, It. J. aritificial Itell. Appl., vol. 3, o. 2, pp , [23] A. Sigh ad S. Kumar, Face Recogitio Usig PCA ad Eige Face approach,, Iteratioal Joural of Computer Applicatios,vol. 2013, pp. 7 12, [24] R. K. Sahu, A Comparative Study of Face Recogitio System Usig PCA ad LDA,, Iteratioal Joural of IT, Egieerig ad Applied Scieces Research, vol. 2, o. 10, [25] F. Idetificatio ad D. U. Eigefaces, Face Idetificatio & Detectio Usig Eigefaces *1,, INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY, vol. 2, o. 1, [26] S. Gupta ad R. Gupta, A Bespoke Approach For Face-Recogitio Usig PCA,, Iteratioal Joural o Computer Sciece ad Egieerig, vol. 02, o. 02, pp , [27] L. C. Paul, A. Al Suma, ad N. Sulta, Methodological Aalysis of Pricipal Compoet Aalysis ( PCA ) Method, Iteratioal Joural of Computatioal Egieerig & Maagemet ", vol. 16, o. 2, , IJCSMC All Rights Reserved 83