33.Real Time Drowsy Driver Detection in Matlab | Wavelet | Principal Component Analysis

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  2007IEEE International Symposium on Signal Processing and Information Technology Drowsy Driver Assistant System M. ElSabrouty, A. Hamdy, A. Fawky and S. Khalil' 1 Faculty of InformationEngineering and Technology CommunicationDepartment German University in Cairo New cairo, Cairo, Egypt. E-mail. maha.elsabrout 4. uc. edu.eg ahmed elsayedna~student.guc. edu._!eg abdeiattah.solimana a-gdn.uc d.e sherifkhail/&student.guc. edu.!eg Many applications were developed for face detection Abstract- This paper proposes a new approach to help the which is Neural Networks (NN), Principle Component drowsy drivers on the roads by developing a real time Analysis (PCA), wavelets, edge detection, Hough transform, computerized system which has the ability to detectthe face and ' . ' . ' . a to know the status of theeyes, if the eyes are closed the system and template matching and geometrical matching are some strikes out analarm toalert the driver. We used PCA and skin techniques taken for major approaches. Neuralnetworks and tone for face detection. For eye detection we used edge detection, matching algorithms needs a huge training set of face and wavelets transform and YCbCr transformation. The algorithm non-face imagesand take a longtime in execution as performs well regardless the face position, lighting varieties and immune to any distracting noise. The designedalgorithm is able discussed in [2],[7] & [18]. Principle component analysis to attain 90% accuracy when it was tested on different takes only gray scale images as well as it is used for databases. recognition as discussed in [5] [6], [10],[13],[15], [20] & [21]. Edge detection is severely affected by light condition as Keywords-Drowsy driver, edge detection, eye detection, face in [3], [4] & [8]. As for wavelets it uses a big size window so detection, Principal Component Analysis, skin tone , YCbCr, wavelets transform it cannot detect small face and fail in extreme lighting condition as discussed in [10] & [22]. In theother hand, eye detection techniques are not as popular as face detection I. INTRODUCTION techniques or it only works with certain conditions. So we had to introduce a new algorithm that could detectthe eyes Sleeping while driving happens more often more than within the faceregion. mostof the people think. According to the United State As our application is online and thus is governed by Department of Transportation (USDT) [11I] & [ 14], execution deadlines, we have proposed a system to improve drowsiness is responsible for 10% to lO0% percent of the 20 millsioness araiesontswhic foccu eachto yearcent the U, the driver's safety by detecting the status of the eye using as million car accidents which occur each year inthe US, PA&si-oefrac dtcintomiizth therefore, developing an alarm system is a crucial task. It is PCA & skin-tone for face detection to minimize the thus important to develop a fast alarmsystem thatwill searchingregion for the eyes. For eye detection, we have notify drivers upon drowsiness and yet produce as few false proposed using wavelets transformation and edge detection alarms as possible. with the addition of YCbCr transformation. At last, we use The human face can be utilized as a preprocessing step to Kalman filters for trackingthe eyes to continuously know the limit thesearch for eyes within a limited space and thus eyesposition and status. The whole alarmsystem is reduce false detection result. Face detection is considered as implemented on the DSP processor, ADSP-BF561 Blackfin the backbone of many systems as in security systems,face Symmetric Multi-Processor for Consumer Multimedia. The recognition and human interface applications. In Drowsy microprocessor contains 512 MB of RAMs and 8 MB of Driver Application, facedetection has to be combined with flash memory. an eye detection algorithm to get the desired performance. 978-1 -4244-1 835-0/07/$25.00 ©C2007 IEEE 176 Authorized licensed use limited to: Bharat University. Downloaded on December 20, 2009 at 03:19 from IEEE Xplore. Restrictions apply.  The rest of the paper is organized as follows. Section 11, chrominanceof the skin toextract some facial features such presents the backgroundon the used algorithms. We present as the mouth or the eyes. our modifications and implementation on the used B. Eye Detection Algorithms algorithms in Section III. Section IV presents the simulation and implementation results. Finally, we conclude the paper in Section V. 1) Edge detection II. THEORETICAL BACKGROUND It may beoneof the easiest ways for face or eye detection[16]that comes into our minds at first as it has been A. Face Detection Algorithms discussedbefore by many researchers. Themain aim of the edge detection techniques is to find points in an input digital 1) Principle Components Analysis image at which the light intensity changessuddenly (sharply). These sharp changes in the image can reveal some As discussed in [13], PCA starts with a random vector x important things from an image like: with n elements, which contain a sample as second order *Non-uniform surface. statistics (SOS). No probabilitydensity of the vectors are *Changes in the property of a material. made, it only matters that the first and second order statistics '~~~~~~~~~~~~~~ Luminance change. are known or can be estimated for vector x, so the elements *~~~~~~~~~~~ Discontinuities in surface orientation. of x are measurements like pixel gray levels or values of a signal at different time instants. It is important that the The edge detection for an image consequently reduces the elements are mutually correlated and there is some size of the image and the amount of data in it; it filters out redundancy in x which makes the compression possible. some unnecessary information. It is easy to be implemented, PCA transformation starts with centering the vector x by because less processing bandwidth is needed, but, it is very subtracting its mean: affected by the luminance conditions, works only on x~x,(-E () grayscale images. This mean is estimated from the available sample x(1), ... ,x(T). After centering the vector, the mean E{x} will 2)Discrete wavelets transform be zero. Then, x is linearly transformed to another vector y In this method [1] we are suggesting a new method for with m elements such that m < n, that leads to induce the redundancyas the corelationsis remove . Thsi.oeb eye recognition using discrete wavelets transform with fidunding y asrathe orthogtional is cordinatesemove. sh tt te b 'Haar' as the mother wavelet.In spite of many researchers elementsaoftatxdinrthegonew coordinateystbeome suncorreatede opinions although it is being computational intensive but it elements of x in the new coordinates become uncorrelated. is powerful as it is based on strong mathematical concepts However, the variances of the projections of x on the new and relations that ensures and proves its reliability and its coordinate axes are maximized so that the first axis corresponds to the maximal variance; the second axis expected efficiency as it is robust against different lighting corresponds to the maximal variance in the direction condition, noise and low computational coast. Wavelets are used not only in eye recognition but also in many other fields orthogonal to the first axis and so on. as it facilitates the analysis of a specific signal. We experienced some difficulties in dealing with wavelets and in 2) Skin-Tone Extraction applying some algorithms to recognize the eyes in an image. Due to the variation in luminance, different environmental Many algorithms dealt before with the skincolor and how conditions, visual angle and facial expressions; facial organs to detectfaces by finding the skin such in [9], [12],[17] & and features are recognized by this order: eyes, nose and [19]. Mostof these techniques were based on finding a face mouth. Factors that must be taken in consideration: pattern by comparing it with a certain mask. However, they eyae e ' yelahes, eylidLs movements, head tiltig,_ makeun, glasses. all start by finding the suitable color space such as YCbCr, HWIU or others. YCbCr is the most used color space as most Of the previously mentioned algorithms operate on the 177 Authorized licensed use limited to: Bharat University. Downloaded on December 20, 2009 at 03:19 from IEEE Xplore. Restrictions apply.  a) Discretised wavelet transforms face edge detection is preformed to detect the edges in the The main reason for using this type of wavelets face. 4) The output of the edge is taken as an input to the transformations is to analyze a specified signal to pick just a wavelet transformation. discrete subset of the upper half plane to be able to A. Face Detection Algorithm reconstruct a signal from the corresponding wavelet .. coefficients. The corresponding discrete subset of the half 1) itnitalization m r The first step iS initialization, by estimating the eigenface plane consists of all thepoints ,. with integers from the database to get the mean position and size of the while m: nI E Z. The corresponding baby wavelets are faces. First, we load the name of the training set from a text known as: file. Then, we get the principle component analysis of the TM = -fT - (2) eigenfaces resulting from thebasic eigenface algorithm. At a>1, b>O last bymanual processing we identify the mean position and For a reconstruction ofany signal x of finite energy by the size of the face. formula: 2) Training :(t) = E ) X, ''6 3, 4} We load the training set from the listfile. Then, the nI.EZ-nEZL (3) training set in cropped to meet the criteria from phase 1. Then, we apply the algorithm discussed in [6]. At the end, b) Mother wavelet we reduce the dimension of the output by choosing the In real-life practical applications, the designers and the principle components. researchers prefer the most efficient ways, so they use 1. The first step was getting the mean of the training continuously differentiable functions withcompact support set. as mother (prototype) wavelet (functions). Therefore, to Al satisfy some analytical requirements we should choose the (8) wavelet functions from a subspace of the space. (8) L(R) n2 , inthis space thefunctions are both 2. Then, we centers the training set around zero by L2R subtractingthe mean from it. absolutely and square integrable: n t m f-rt. /~ ~ ~~~~~~ ~~~~~D = Fi T (t) I' dt < oc ~~~~~~~~~~(9) -4tH2dt K (4) 3. Afterwards, we calculate the eigenvectors and the J K(t)dt < (5) eigenvalues of the covariance the matrix. It can be ensured that we can formulate the conditions of C Ln= '?3+ zero mean and square norm one: = AT .C (10) _00 0(0t dt =O 0 (6) Where A-[1 42 .4M1 f/ K(t)Kdt = (7) 4. Then finally, we get theprinciple components of In the case of continuouswavelet transform, the T must the eigenfaces depending on its eigenvalues. be a wavelet. The mother wavelet must satisfy an 3) Image Preprocessing admissibility criterion to get a stably invertible transform. We insert any color image as an input image. This image is In the case of discrete wavelet transform, the multi- resized to a 256x256 pixel image. Then, the image is resolution analysis is used in many constructions, whichnormalized to adjust the lightening condition. Afterwards, defines the wavelet by a scaling function. we apply a skindetection algorithm to eliminatethe background from the resized image. At the end, the image is III. SYSTEM ALGORITHM transformed into a grayscale image of PGM format. Our algorithm containsfour main techniques: 1) we uses a 4) Face C i ProjectIon skin-tone extraction as a preprocessing step. 2) Then, we use The gray scaled image is scanned for collecting samples. principle component analysis to successfully detecttheface These samples are subtracted by thetraining set mean. And using YaleFace Image Database B. 3) After detectingthe 178 Authorized licensed use limited to: Bharat University. Downloaded on December 20, 2009 at 03:19 from IEEE Xplore. Restrictions apply.  then, the output samples are projected on the reduced color in the cropped region and we took thehighest 10% of dimension matrix from the training phase. them to lower the number of eye locations 5) Estimation 8) Locating the eyes For the final phase, theprojection matrix is sorted in After getting 10% of the croppedimages we then make ascending order and we get the smallestprojection as our sure that there is no eye position close to theother (we final output of theface and draw arectangle of fixed size choose 5 pixels) then we cropped these position 20X20 ,of 150x180 pixel. theeye-position in 512X512 colored image ,to look for white region in this area which will be identified as the eye if not B. Eye Detection Algorithm foundan alarm will sound. 1) Normalization Due to the undesirable effects of the luminance the YCbCr code gives us undesirable output, so we decided to IV. EXPERIMENTAL RESULTS use a normalization code to normalize the luminanceof the As we can see in the below output image, acceptable eye input picture. detection is performed despite some facial conditions like 2) Edge Detection status of the eyes, the existence of glasses, different skin Edge detection as mentionedwas our first approach but as colors, and head positions. This algorithm hasproven we witnessed it was not thebest approach when it comes to flexibility and reliability. If we use perfect background and eye detection due to luminance and other factors but after all perfectfacedetectionthe efficiency will increase up to 9500 it gave us the edges of the eye so we decided that it will be our input to the wavelet transform, so that it will make the eye socket detection easier. 3) YCbCr transformation We developed an algorithm that has the ability to identify the human skincolor and it turns it to blue basedon certainthresholds. Its main idea that it converts the input RGB image to YCbCr space, using for loops and conditional if- R statements it could set the skin color into blue (value: 255), and it cancels any objects at the background (value: 0). 4) DWT Themain aim of this function is to compute the second order DWT of an input matrix (image). We did it on two steps, first we calculated the DWT coefficients using 'haar' method, second, we recomputed the second order DWT of the computed coefficients, then we took the absolute value Fig2 Successful eye detection the second coefficients ina matrix (Z). V. CONCLUSION AND FUTURE WORK 5) Performing IDWT We have presented an algorithm for Drowsy Driver Computing the inverse DWT of the image. We calculated the Assistant System that detect skins as apre-processing step addition of the selected node from the wavelet tree and its and then apply PCA rules to detectfacessuccessfully. The maxima. algorithm begins with initialization and then training which 6) Combination Between YCbCr & Wavelets. are applied only once. Then, we taketheinput image are Given the output of the wavelet we combined the wavelet preprocess it to getaskin only image in grayscale. The to YCbCr by adding the two images together. image is then sampledand project on the trainingspace. 7) Cropping the eyeregion Finally, an estimation phase is applied to detect the face. After combining the YCbCr with wavelet transform all After getting the face, the algorithm performs edge detectionpossibleeye position is found but that was it not enough we and wavelet transformation to detectthe eye position. At last, still had to chose from the many eye position selected .To do we zoom into the eye position to know the status of the eye that we cropped 1 Oxi 0 image of the YCbCr of every selected by finding the white components in it. eye position, then we calculatedthe percentage of white 179 Authorized licensed use limited to: Bharat University. Downloaded on December 20, 2009 at 03:19 from IEEE Xplore. Restrictions apply.
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