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National Conference on Role of Cloud Computing Environment in Green Communication 2012 717 Feature extraction for face recognition by using a novel and effective color boosting S.S. Sugania V.R. Bhuma M.E II M.E- CSE Ass.Prof Vins Christian Collage of Engineering Vins Christian Collage of Engineering Abstract-This paper introduces the new the existing color FR methods are restricted to color face recognition (FR) method that using a fixed color-component configuration makes effective use of boosting learning as comprising of only “two” or “three”[1] color color-component feature selection components . In particular, currently used color- framework. The proposed boosting color- component choices are mostly made through a component feature selection framework is combination of intuition and empirical designed for finding the best set of color- comparison without any systematic selection component features from various color spaces strategy.As such, existing methods may have a (or models), aiming to achieve the best FR limitation to attaining the best FR result for performance for a given FR task. In addition, given FR task. This is because specific color to facilitate the complementary effect of the components effective for a particular FR selected color-component features for the problem could not work well for other FR purpose of color FR, they are combined using problems under other FR operating conditions the proposed weighted feature fusion scheme. (e.g., illumination variations) that differ from The effectiveness of my color FR method has those considered during the process of been successfully evaluated on the following determining specific color components. five public face databases (DBs): CMU-PIE, Hence, the important issue in color FR is: how Color FERET, XM2VTSDB,SCface, and can one select the color components from FRGC 2.0. Experimental results show that various color models in order to achieve the best the results of the proposed method are FR performance for the specific FR task?In this impressively better than the results of other paper, to cope with the aforementioned issue, I state-of-the-art color FR methods over propose a new color FR method. My method different FR challenges including highly takes advantage of “boosting” learning as a uncontrolled illumination, moderate pose feature selection mechanism,aiming to find the variation, and small resolution face images. optimal set of color-component features for the Index Terms—Boosting learning, color face purpose of achieving the best FR result[2]. To recognition, color space,color the best of our knowledge, my work is the first component,feature selection. attempt to incorporate feature selection scheme underpinning boosting learning into FR methods I.INTRODUCTION using color information. A facial recognition system is a computer RECENTLY, considerable research work in application for automatically identifying or face recognition (FR) has shown that facial color verifying a person from a digital image or a information can be used to considerably improve video frame from a video source. One of the FR performance, compared to the FR methods ways to do this is by comparing selected facial relying only on grayscale information. Most of features from the image and a facial database.It Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 718 is typically used in security systems and can be framework. In particular, this section details the compared to other biometrics such as proposed selection criterion. In Section III, I fingerprint[2] or eye iris recognition systems. explain the proposed modules for a FR The remaining of the paper is organized as purpose.Conclusions are presented in Section follows. Section II describes feature extraction IV. for face recognition within boosting learning Fig 1:Proposed color framework model II.FEATURE EXTRACTION FOR termed “learning set,” the effective selection FACE RECOGNITION criterion is proposed. The proposed selection criterion is in the form of penalty-based objective function with its associated weighting In this paper, a multiclass boosting parameter for the purpose of selecting color- “Adaboost.M2” framework is adapted to component features which not only produce implement color-component feature selection. small classification errors, but also keep their Differing from other boosting learning mutual dependence low. The proposed selection frameworks,the key advantage of Adaboost.M2 criterion is highly useful for achieving a low framework is to force the weak learners to generalization classification error. In addition, to concentrate not only on the hard instances (or perform color FR, the color-component features patterns), but also on the incorrect class labels chosen via our boosting framework are that are hardest to classify. Overall framework combined at the feature level. Specifically, of the proposed color FR method which largely selected color-component features are fused consists of two parts:1) color-component feature based on weighted feature fusion scheme selection with boosting, 2) color FR solution depending upon the associated confidence of using selected color component features. each color-component feature for achieving To determine the best color component feature better FR performance. at each boosting round for recognizing the hard- In order to evaluate the effectiveness of the to-classify sample subset of a training set, proposed color FR method, comparative and Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 719 extensive experiments have been carried out. For When performing YCbCr to R´G´B´ this, five public face databases (DB) CMU- conversion using the above equations, note that PIE,Color FERET,XM2VTSDB,SCface,FRGC the resulting R´G´B´ values have a nominal 2.0 are used. Experimental results show that the range of 16-235 (black-white). Occasional results of the proposed method are impressively excursions into the 0-15 and 236-255 values are better than the results of other state-of-the-art possible due to Y and CbCr occasionally going color FR methods over different FR challenges outside the 16-235 and 16-240 ranges, including highly uncontrolled illumination, respectively, due to video processing.Rounding moderate pose variation, and small resolution errors and noise. face images. However, if the 24-bit R´G´B´ data are to The remaining of the paper is organized as have a range of 0- 255 (black-white), as is follows. Boosting color-component feature commonly found in PCs, the following selection describes our color-color component equations (used by the HMP8115) should be feature selection method within boosting used to maintain the correct black and white learning framework. In particular, this section levels: details the proposed selection criterion. In color FR using selected color-component features, we Y = 0.257R´ + 0.504G´ + 0.098B´ + 16 explain the proposed weighted feature fusion Cb = -0.148R´ - 0.291G´ + 0.439B´ + approach to combining selected color- 128 component features for a FR purpose. In Cr = 0.439R´ - 0.368G´ - 0.071B´ + 128 experiments, we present extensive and R´ = 1.164(Y - 16) + 1.596(Cr - 128) comparative experimental results that G´ = 1.164(Y - 16) - 0.813(Cr - 128) - demonstrate the effectiveness of the proposed 0.392(Cb - 128) color FR method. Conclusions and directions for B´ = 1.164(Y - 16) + 2.017(Cb - 128) future research are presented in conclusion and future research. For the YCbCr to R´G´B´ equations, the R´G´B´ values must be saturated at the 0 and A.RGB Generation 255 levels due to occasional excursions outside the nominal YCbCr ranges. BT.601 defines Y to have a nominal range of B. Linear RGB Generation 16-235 (blackwhite); Cb and Cr are defined to have a nominal range of 16- 240, with 128 PCs usually prefer to use the linear RGB data corresponding to zero. YCbCr is also defined to format due to the amount of software already have been derived from gamma-corrected RGB written and the simplified algorithms. Gamma (R´G´B´) data. The BT.601 quations are used by correction for the display monitor may then done many video ICs to convert between digital real-time in the GUI acceleration chip. R´G´B´ data and YCbCr are: Therefore, it may be desirable to remove the gamma information from the R´G´B´ data. Y = (77/256)R´ + (150/256)G´ + NTSC video is pre-corrected using a gamma of (29/256)B´ 2.2. Thus, to generate 24-bit linear RGB data: Cb = -(44/256)R´ - (87/256)G´ + for (R´, G´, B´) < 21 (131/256)B´ + 128 Cr = (131/256)R´ - (110/256)G´ - R = ((R´/255) / 4.5) * 255 (21/256)B´ + 128 G = ((G´/255) / 4.5) * 255 R´ = Y + 1.371(Cr - 128) B = ((B´/255) / 4.5) * 255 G´ = Y - 0.698(Cr - 128) - 0.336(Cb - 128) for (R´, G´, B´) ≥ 21 B´ = Y + 1.732(Cb - 128) R = 255* (((R´/255) + 0.099) / 1.099)2.2 Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 720 G = 255 * (((G´/255) + 0.099) / 1.099)2.2 aims at making optimal balance between B = 255 * (((B´/255) + 0.099) / 1.099)2.2 classification error and the degree of mutual dependence among selected FR learners. PAL video specifies a gamma of 2.8, although a Here, using classification error for is value of 2.2 is now commonly used. If the video calculated based on “pseudo-loss” where is the is pre-corrected using a gamma of 2.8, the mislabel weight vector.Note that for computing , following equations may be used to generate 24- both hard-to-classify samples and hard-to- bit linear RGB data: separate pairs of class labels are taken into account at the same time. R = 255 * (R´/255)2.8 G = 255 * (G´/255)2.8 B = 255 * (B´/255)2.8 III. EXPERIMENTS Many modern PAL video decoders, such as the Most of the existing color FR methods are HMP8115, allow the selection of either the 2.2 restricted to using a fixed color-component or 2.8 gamma factor to be used for calculations. configuration comprising of only “two” or “three” color components. In particular, C. Proposed Selection Criterion currently used color-component choices are mostly made through a combination of intuition At each boosting round, the best FR learner and empirical comparison, without any (i.e., the best color-component feature) should systematic selection strategy. As such, existing be determined from among constructed FR methods may have a limitation to attaining the learners , each of which depends upon a single best FR result for given FR task. This is because color-component feature. To this end, a selection specific color components effective for a criterion plays a crucial role in determining the particular FR problem could not work well for “goodness”of feature selection. in ensemble other FR problems under other FR operating classification (including boosting), it has been conditions (e.g., illumination variations) that shown that, to achieve the lowest generalization differ from those considered during the process error, we need to create ensembles (or of determining specific color components. classifiers) with low training classification error, while at the same time their mutual dependence The existed color FR method which largely should be kept minimal. consists of two parts: 1) Color-component In particular, in our feature selection problem, feature selection with boosting, 2) Color FR mutual dependence between color-component solution using selected color component features have to be carefully considered as features. different color channels may have similar properties from the view-point of classification. We propose a new color FR method. Our For instance, the and channels (from and color method takes advantage of “boosting” learning spaces, respectively) both encode the intensity as a feature selection mechanism, aiming to find information for green colors. the optimal set of color-component features for Therefore, before a FR learner is selected, the purpose of achieving the best FR result with mutual dependence between the new FR learner the help of DWT,Eigenface ,Face congruency. and each of the selected FR learners should be To the best of our knowledge, our work is the examined to ensure that the complementary first attempt to incorporate feature selection information (that improves classification) scheme underpinning boosting learning into FR carried by the new FR learner is not captured by methods using color information. the preceding FR learners before. To address the aforementioned issue, we A.Discrete Wavelet Transform develop an effective selection criterion which Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 721 The field of Discrete Wavelet Transforms equal to 1, and its corresponding (normalized) (DWTs) is an amazingly recent one. The basic eigen vector contains, as its components, the Principles of wavelet theory were put forth in a value of the <f> function at integer values of x. paper by Gabor in 1945 , but all of the definitive Once these values are known, all other values of papers on discrete wavelets, an extinction of the function <f>(x) can be generated by Gabor's theories involving functions with applying the recursion equation to get values at compact support, have been published in the half integer x, quarter-integer x, and so on down past three years. Although the Discrete Wavelet to the desired dilation. This effectively Transform is merely one more tool added to the determines the accuracy of the function toolbox of digital signal processing, it is a very approximation. important concept for data compression. Its utility in image compression has been electively This class of wavelet functions is demonstrated. This paper discusses the DWT constrained, by definition, to be zero outside of a and demonstrates one way in which it can be small interval. This is what makes the wavelet implemented as a real-time signal processing transform able to operate on a finite set of data, a system. Although this paper will attempt to property which is formally called "compact describe a very general implementation, the support." Most wavelet functions, when plotted, actual project used the STAR Semiconductor appear to be extremely irregular. This is due to SPROC lab digital signal processing system. the fact that the recursion equation assures that a wavelet <p> function is non-differentiable A wavelet, in the sense of the Discrete everywhere. The functions which are normally Wavelet Transform (or DWT), is an orthogonal used for performing transforms consist of a few function which can be applied to a finite group sets of well-chosen coefficients resulting in a of data. Functionally, it is very much like the function which has a discernible shape. Discrete Fourier Transform, in that the The Mallat "pyramid" algorithm is a transforming function is orthogonal, a signal computationally efficient method of passed twice through the transformation is implementing the wavelet transform, and this is unchanged, and the input signal is assumed to be the one used as the basis of the hardware a set of discrete-time samples. Both transforms implementation. The lattice filter is equivalent to are convolutions. Whereas the basis function of the pyramid algorithm except that a different the Fourier transform is a sinusoid, the wavelet approach is taken for the convolution, resulting basis is a set of functions which are defined by a in a different set of coefficients, related to the recursive difference equation, usual wavelet coefficients ck by a set of transformations. Φ(x)= (1) The Pyramid Algorithm Where the range of the summation is determined by the specified number of nonzero The pyramid algorithm operates on a finite coefficients M. The number of nonzero set of N input data, where N is a power of two; coefficients is arbitrary, and will be referred to this value will be referred to as the input block as the order of the wavelet. The value of the size. These data are passed through two coefficients is, of course, not arbitrary, but is convolution functions, each of which creates an determined by constraints of orthogonality and output stream that is half the length of the normalization. A good way to solve for values of original input. These convolution functions are equation (1) is to construct a matrix of filters; one half of the output is produced by the coefficient values. This is a square M x XI “low pass” filter function,related to equation (1): matrix where M is the number of nonzero coefficients. The matrix is designated L, with entries .This matrix always has an eigen value Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 722 = i=1,……, consists of sine functions, whose frequencies are odd multiples of the fundamental frequency. At (2) the rising edges of the square wave, each sinusoidal component has a rising phase; the and the other half is produced by the “high pass” phases have maximal congruency at the edges. filter function, This corresponds to the human-perceived edges in an image where there are sharp changes = , i=1,……, between light and dark. Congruency of phase at any angle produces a (3) clearly perceived feature . The angle at which the congruency occurs dictates the feature type, Where Z is the input block size, c are for example, step or delta. The Local Energy the coefficients, a and b are the output functions. Model was developed by Morrone et al and (In the case of the lattice filter, the low- and Morrone and Owens. Other work on this model high-pass outputs are usually referred to as the of feature perception can be found in Morrone odd and even outputs, respectively.) The and Burr, Owens et al., Venkatesh and Owens, derivation of these equations from the original and Kovesi. The work of Morrone and Burr has <p and ip equations can be found. In many shown that this model successfully explains a situations, the odd or low-pass output contains number of psychophysical effects in human most of the "information content" of the original feature perception. The local, complex valued, input signal. The event or high-pass output Fourier components at a location x in the signal contains the difference between the true input will each have an amplitude An(x) and a phase and the value of the reconstructed input if it angle Án(x). The magnitude of the vector from were to be reconstructed from only the the origin to the end point is the Local Energy, information given in the odd output. In general, jE(x)j. The measure of phase congruency higher-order wavelets (i.e., those with more non- developed by Morrone et al is zero coefficients) tend to put more information into the odd output, and less into the even (x)= output. If the average amplitude of the even output is low enough, then the even half of the signal may be discarded without greatly Under this definition phase congruency affecting the quality of the reconstructed signal. is the ratio of jE(x)j to the overall path length An important step in wavelet-based data taken by the local Fourier components in compression is finding wavelet functions which reaching the end point. If all the Fourier cause the even terms to be nearly zero. components are in phase all the complex vectors would be aligned and the ratio of jE(x)j=Pn B.Phase Congruency An(x) would be 1. If there is no coherence of phase Phase congruency is a measure of feature significance in computer images, a method of edge detection that is particularly robust against changes in illumination and contrast.Phase congruency reflects the behaviour of the image in the frequency domain. It has been noted that edge like features have many of their frequency components in the same phase. The concept is similar to coherence, except that it applies to functions of different wavelength.For example, the Fourier decomposition of a square wave Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 723 Figure 2:Polar diagram faces. Any human face can be considered to be a combination of these standard faces. For Polar diagram showing the Fourier example, one's face might be composed of the components at a location in the signal plotted average face plus 10% from eigenface 1, 55% head to tail. The weighted mean phase angle is from eigenface 2, and even -3% from eigenface given by A(x). The noise circle represents the 3. Remarkably, it does not take many eigenfaces level of E(x) one can expect just from the noise combined together to achieve a fair in the signal.The ratio falls to a minimum of 0. approximation of most faces. Also, because a Phase congruency provides a measure that is person's face is not recorded by adigital independent of the overall magnitude of the photograph, but instead as just a list of values signal making it invariant to variations in image (one value for each eigenface in the database illumination and/or contrast. Fixed threshold used), much less space is taken for each person's values of feature significance can then be used face. over wide classes of images. The measure of phase congruency does not provide good localization and it is also sensitive to noise. Kovesi developed a modified measure consisting of the cosine minus the magnitude of the sine of the phase deviation; this produces a more localized response. This new measure also incorporates noise compensation.A small constant is incorporated to avoid division by zero. Only energy values that exceed T, the estimated noise influence, are counted in the result. The symbols b c denotes that the enclosed quantity is equal to itself when its value is positive, and zero otherwise. In practice local frequency information is Fig 3.Eigen face reconstruction obtained via banks of Gabor wavelets tuned to different spatial frequencies, rather than via the Fourier transform. The appropriate noise threshold, T is readily determined from the To create a set of eigenfaces, one must: statistics of the filter responses to the image. Prepare a training set of face images. The Phase congruency is a measure of feature pictures constituting the training set should have significance in computer images, a method of been taken under the same lighting conditions, edge detection that is particularly robust against and must be normalized to have the eyes and changes in illumination and contrast. mouths aligned across all images. They must also be all resampled to a C.Eigen face common pixel resolution (r × c). Each image is treated as one vector, simply Eigenfaces are a set of eigenvectors used in by concatenating the rows of pixels in the the computer vision problem of human face original image, resulting in a single row recognition. A set of eigenfaces can be with r × c elements. For this implementation, it generated by performing a mathematical process is assumed that all images of the training set are called principal component analysis (PCA) on a stored in a single matrix T, where each row of large set of images depicting different human the matrix is an image.Subtract the mean. The faces. Informally, eigenfaces can be considered average image a has to be calculated and then a set of "standardized face ingredients", derived subtracted from each original image from statistical analysis of many pictures of in T.Calculate the eigenvectors and Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 724 eigenvalues of the covariance matrix S. Each components in the same phase. Facial eigenvector has the same dimensionality recognition was the source of motivation behind (number of components) as the original images, the creation of eigenfaces. For this use, and thus can itself be seen as an image. The eigenfaces have advantages over other eigenvectors of this covariance matrix are techniques available, such as the system's speed therefore called eigenfaces. They are the and efficiency. Using eigenfaces is very fast, and directions in which the images differ from the able to functionally operate on lots of faces in mean image. Usually this will be a very little time. computationally expensive step (if at all possible), but the practical applicability of ACKNOWLEDGEMENT eigenfaces stems from the possibility to compute the eigenvectors of S efficiently, without ever First and foremost I acknowledge the abiding computing S explicitly, as detailed presence and the abounding grace of our below.Choose the principal components. almighty god for this unseen hand yet tangible The D x D covariance matrix will result guidance all through the formation of this in D eigenvectors, each representing a direction project. I express my sincere gratitude to our in the r × c-dimensional image space. The Chairman, Thiru. Nanjil M. Vincent ,B.A.,B.L., eigenvectors (eigenfaces) with largest associated Ex.M.P for providing me all supports.I am eigenvalue are kept. Unfortunately, this type of extremely grateful to our Principal, Dr.B.Sasi facial recognition does have a drawback to Kumar, M.E., Ph.D., for his inspiration to me for consider: trouble recognizing faces when they preceding this project. I would like to are viewed with different levels of light or express my heartfelt thanks to our Head of the angles. For the system to work well, the faces Department, Mr.K.John peter, M.Tech., for his need to be seen from a frontal view under interest in my project for his valuable similar lighting. Face recognition using suggestions.I am grateful to my Internal guide, eigenfaces has been shown to be quite accurate. Mrs.V.R.Bhuma, M.E.,lecturer., for her Suggestion, motivation and dedicated guidance in seeking this project work to its completion. I IV.CONCLUSION express my heartiest thanks to all other staffs of In this paper, a novel and effective color FR computer science and engineering who have method is proposed. It is based on the selection helped me in one way or other for the successful of the best color-component features (from completion of the project. I express my thanks various color models) using the proposed variant to my beloved friends for the kind co-operation of boosting learning framework by discrete and their continuous encouragement. Finally I wavelet transform , Phase Congruency and eigen express my thanks to my beloved parents. face. These selected color component features are then combined into a single concatenated color feature using weighted feature fusion. This REFERENCES makes the FR method to be effective. For an input represented by a list of 2n numbers, the Haar wavelet transform may be considered to [1] “Discrete Wavelet Transforms: Theory and simply pair up input values, storing the Implementation” Tim Edwards difference and passing the sum. This process is (tim@sinh.stanford.edu)Stanford University, repeated recursively, pairing up the sums to September 1991. provide the next scale: finally resulting in 2n − 1 [2] “Phase Congruency Detects Corners and differences and one final sum. Phase congruency Edges “ Peter Kovesi School of Computer reflects the behaviour of the image in the Science & Software Engineering The University frequency domain. It has been noted that edge of Western Australia Crawley, W.A. 6009. like features have many of their frequency Department of CSE, Sun College of Engineering and Technology National Conference on Role of Cloud Computing Environment in Green Communication 2012 725 [3] ” Color Face Recognition for Degraded Face Images” Jae Young Choi, Yong Man Ro, Senior Member, IEEE, and Konstantinos N. (Kostas) Plataniotis, Senior Member, IEEE. [4] ” Boosting Color Feature Selection for Color Face Recognition” Jae Young Choi, Student Member, IEEE, Yong Man Ro, Senior Member, IEEE, and Konstantinos N. Plataniotis, Senior Member, IEEE. [5] “A Decision-Theoretic Generalization of On- Line Learning and an Application to Boosting”Yoav Freund and Robert E. Schapire- AT6T Labs, 180 Park Avenue, Florham Park, New Jersey. . Department of CSE, Sun College of Engineering and Technology

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