Tips & Tricks
Like this document? Why not share!
A Literature review on Facial Expre...
Face Recognition by Sumudu Ranasinghe
Face detection and recognition
by Derek Budde
Pose and Illumination in Face Recog...
Face detection using the 3 x3 block...
Email sent successfully!
Show related SlideShares at end
Jan 16, 2014
News & Politics
Comment goes here.
12 hours ago
Are you sure you want to
Your message goes here
Be the first to comment
Be the first to like this
Number of Embeds
No notes for slide
Transcript of "50220130402003"
1. International Journal of Graphics and Multimedia OF GRAPHICS (IJGM), 0976 – 6448(Print), INTERNATIONAL JOURNALDecember ISSN © IAEME AND ISSN 0976 – 6456(Online) Volume 4, Issue 2, May 2013, MULTIMEDIA (IJGM) ISSN 0976 - 6448 (Print) ISSN 0976 -6456 (Online) Volume 4, Issue 2, May - December 2013, pp. 20-30 © IAEME: www.iaeme.com/ijgm.asp Journal Impact Factor (2013): 4.1089 (Calculated by GISI) www.jifactor.com IJGM ©IAEME TEXTURE ANALYSIS FOR FACE RECOGNITION J. V. Gorabal Associate Professor, CSE SCEM, Mangalore Manjaiah D. H. Professor, Computer Science Department. Mangalore University, Mangalore ABSTRACT A new approach for face recognition using wavelet features is presented. Initially, the given image is divided into 12 blocks, each of size 50*60 pixels. Then, discrete wavelet transform is applied to each block and energy features (mean) of horizontal and vertical coefficients are determined. The extracted features from training samples are used to train the neural network. Further, the test face image is processed to obtain wavelet energy features and recognized using neural network classifier. Keywords: Wavelet energy features, Neural Network, Face Recognition 1. INTRODUCTION Pattern recognition is a day machine intelligence problem with numerous applications in a wide field, including Face recognition, Character recognition, Speech recognition as well as other types of object recognition. The field of pattern recognition is still very much in it is infancy, although in recent years some of the barriers that hampered such automated pattern systems have been lifted due to advance in computer hardware providing machines capable of faster and more complex computation. Humans do face recognition on regular basis naturally and so effortlessly that we never think of what exactly we the looked at in the face. Face is a dimensional object that is subjected to varying illumination, poses, expressions and so on its two dimensional image. Hence, Face recognition is an intricate visual pattern problem which can be operated in these modes. 20
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME • • • Face Verification (or Authentication) : That compares a query face image against a template face image whose identity is being claimed (i.e; one to one). Face Identification (or Recognition) That compares a query face image against all the template images in the database to identify the query face. Watch list that compares a query face image only to a list of suspects. Authentication plays a very critical role in security-related applications like Ecommerce. The previously used authentication techniques like security pass or password will not provide absolute confidence as they could be stolen and passwords are sometimes (unwisely) written down. To overcome this drawback biometric security systems are uses. The primary benefit to using face recognition is that facial features are more distinct from one person to another person and these features have scored highest compatibility in Machine Readable Travel Documents (MRTD), hence we go for face recognition. The applications of face recognition are, • Identity verification for physical access control in buildings or security areas is one of the most common face recognition applications. • To allow secure transactions through the Internet, face verification may be used instead of electronic means like password or PIN numbers, which can be easily stolen or forgotten. • Face identification has also been used in forensic applications for criminal identification (mug-shot matching) and surveillance of public places to detect the presence of criminals or terrorists (for example in airports or in border control). • It is also used for government application like national ID, driver`s license, password and border control, immigration, etc. The rest of the paper is organized as follows; the detailed survey related to character recognition of text in scene images is described in Section 2. The proposed method is presented in Section 3. The experimental results and discussions are given in Section 4. Section 5 concludes the work and lists future directions of the work. 2. RELATED WORKS The Face Recognition is of the most difficult task, we have many approaches proposed for the feature extraction .This paper explores the use of morphological operators for feature extraction in range images and curvature maps of connected part They describe two general procedures. The first is the identification of connected part boundaries for convex structures, which is used to extract the node outline and the eye socket outlines of the face. The part boundaries are dined locally based on minima of minimum principal curvature on the surface. The locus of these points suggests boundary lines which surround most convex regions on the surface. However, most of these boundaries are not completely connected. To remedy this problem, w developed a general two-step connection procedure: the partial boundaries are first dilated in such a way that the gaps between them are led. Second, the resulting dilated outlines are skeletonized with the constraint that the pixels belonging to the original boundary parts cannot be removed. 21
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME Feature extraction based on descriptive statistics . This paper proposes a new method of feature extraction for face recognition based on descriptive statistics of a face image. This method works by first converting the face image with all the corresponding face components such as eyes, nose and mouth to grayscale images. The features are then extracted from the grayscale image, based on descriptive statistics of the image and its corresponding face components. The edges of a face image and its corresponding face components are detected by using the canny algorithm. In the recognition step, different classifiers such as Multi Layer Perception (MLP), Support Vector Machine (SVM), kNearest Neighbors (k-NN) and Pair Opposite Class-Nearest Neighbor (POC-NN) can be used for face recognition. They evaluated this method with more conventional eigenface method based upon the AT & T and Yale face databases. The evaluation clearly confirm that for both databases our proposed method yields a higher recognition rate and requires led computational time than the eigen face method. A method to extract facial features using improved deformable templates is described . This method include two steps, first locating features using rectangle templates designed by myself; them, extracting features using deformable templates. In the first step, they get rectangle block including facial features from facial images, the rectangle block is our template to locate features. In the second step extracting features, they describe the features of interest by a parameterized template, they design energy function which links with edges, weighted grads, weighted variance and etc, when the energy function gets its minimum, the parameter values can be a good description for facial feature. The experiment results show that this arithmetic can extract facial feature better and more quickly. A novel face recognition method based on Gabor-wavelet and linear discriminate analysis (LDA) is proposed in . Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images on to discriminant vectors. Second, discriminant vectors are used to select disrciminant pixels, the number of which is much less than that of a whole image. Gabor features are extracted only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of LDA features. Hidden Markov model (HMM) is a promising method  that works well for images with variations in lighting, facial expression, and orientation. Face recognition draws attention as a complex task due to noticeable changes produced on appearance by illumination, facial expression, size, orientation and other external factors. To process images using HMM, the temporal or space sequences ate to be considered. In simple term HMM can be defined as set of finite states with associated probability distributions. Only the outcome is visible to the external user not the and hence the name Hidden Markov Model. The work in the method deals with various techniques and methodologies used for resolving the problem. A face recognition system for personal identification and verification using Genetic Algorithm and Back-propagation Neural Network is proposed . The system consists of three steps. At the very outset pre-processing are applied on the input image. Secondly face feature are extracted, which will be taken as the input of the Back-propagation Neural Network (BPN) and Genetic Algorithm (GA) in the third step and classification is carried out by using BPN and GA. The proposed approaches are tasted on a number of face images. Experimental results demonstrate the higher degree performance of this algorithm. 22
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME Biometric technology has been frequently utilized be researchers in identifying and recognizing human features . This technology identifies human`s unique and static body parts, such fingerprints, eyes, and face. The identification and recognition of a human face use face features` processing and analysis. This consists of determining face components` region and their characteristics, which establishes the role of individual component in face recognition. This research develops a system that separates face features into face components, and extracts the eyes, nose, mouth and face boundary. This process conducted on a frontal single still image. Distances between components are measure, and then combined with other features to construct face semantic. Distances between features are determined by going through the process of face detection based on skin color, cropping to normalize face region, and extraction of eyes, nose and mouth features. This research shows that the determination of face features and face components` distances can be used to identity a face a subsystem of a face recognition system. In this face recognition research , the head is fixed when a photograph is taken. The infrared diodes provide the only illumination. In front of the CCD camera, a light filter lens is used to filter all other light. After the photograph is taken, the eyebrows, eyes, lips, and contour are extracted separately. The shape, size, object-to-object distance, center and orientation are found for each extracted object. The techniques to solve the object shifting and rotating problem are investigated. Image subtraction is used to examine the geometric difference of the two different faces. The obtained classifying data in this research can accurately classify different people`s faces. We propose a fast and improved facial feature extraction technique  for embedded face-recognition applications. First, we introduce local texture attributer to a statistical face model. A texture attribute characterizes the 2-D local feature structures and is used to guide the model deformation. This provides more robustness and faster convergence than with conventional ASM (Active Shape Model). Second, the local texture attributes are modeled by Haar-wavelets, yielding faster processing and more robustness with respect to low-quality images. Third, we use a gradient-based method for model initialization, which improves the convergence. We have obtained good results dealing with test faces that are quite dissimilar with the faces used for statistical training. The convergence area of our proposed method almost quadruples compared to ASM. The Haar-wavelet transform successfully compensates for additional cost of using 2-D texture features. The algorithm has also been tested in practice with a webcam, giving (near) real-time performance and good extraction results. The extraction of required features from the facial image is an important primitive task for face recognition. The paper  evaluates different nonlinear feature extraction approaches, namely wavelet transform, radon transform and cellular neural networks (CNN). The scalability of the linear subspace techniques is limited as the computational load and memory requirements increase dramatically with the large database. In this work, the combination of radon and wavelet transform based approach is used to extract the multiresolution features, which are invariant to facial expression and illumination conditions. The efficiency of the stated wavelet and radon based nonlinear approaches over the databases is demonstrated with the simulation results performed over the FERET database. This paper also presents the use of CNN in extracting the nonlinear facial features. The detailed description of the proposed methodology is given in the next section. 23
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME 3. PROPOSED METHODOLOGY FOR FACE RECOGNITION The proposed method uses feed-forward back propagation neural network based classifier for classification. The methodology is shown in Fig 1. The method involves two phase namely training phase and testing phase. The detailed description of each phase is given in the following sub sections. 3.1 Training This involves processing of images of different person with different expressions, extracting their features and finally developing suitable neural network models which recognize the different persons. The classification makes use of features extracted using discrete wavelet transform approach form face image samples. The original images are converted into gray scale images. Each image is divided into 12 blocks of size 50*60.For each block Discrete Wavelet Transform is applied. 24 features, 2 from each block are extracted. The neural network architecture that is most commonly used with the back propagation algorithm is the multilayer feed-forward network. In training phase the artificial neural network is trained using Back Propagation feed forward neural model. Two pair of files, “input” and “output” are generated. These two pair of files is then given to the neural network which then trains itself accordingly. The training takes place such that the neural network learns that the neural network learns that each entry in the input file has a corresponding entry in the output file. Fig .1. Proposed Block Diagram for Recognition of face 3.2 Testing In testing input image from testing set is selected and its features are extracted and given them to the trained model, the trained ANN model classifies given sample as corresponding person. 3.3 Database Face images of 20 different person with 20 different expressions are collected. Each image is of size 200*180 in .jpg format. Database consists of frontal face images and with same background. The sample images are shown in Fig 2. 24
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME Fig .2. Sample Images 3.4 Feature Extraction A pattern is a quantitative or structural description of an object or an entity of interest in an image. One or more descriptors of an object or an entity an image from the pattern. In other words, a pattern is an arrangement of descriptors. The descriptors also called features in pattern recognition literature. The features are necessary for differentiating one class of objects from another. A method must be used for describing the objects so that features of interest are highlighted. The description is concerned with extracting of features from the object/entity of an image. Algorithm for feature extraction Input : sample image Output : Array containing features Step 1 : Convert the RGB in to gray-scale image Step 2 : Divide the image into 12 blocks of size 50*60 Step 3 : for i=1 to 12 Apply dwt2 for block Calculate the energy function of horizontal and vertical co-efficient End. Step 4 : These co-efficient are stored in an array. Each image is of size 200*180. The original image is converted into gray scale image. Each image is divided into 12 blocks each of size 50*60. For each block, Discrete Wavelet Transform is applied. It computes approximation coefficients matrix and details coefficients matrices (horizontal, vertical, and diagonal, respectively), of each block of the image. The next page shows the 12 blocks with first level decomposition. The discrete wavelet transformation is applied using the function dwt2 [ a h v d ] = dwt2 (m, `haar`); Where m = block image a = approximation co-efficient,h= horizontal co-efficient v= vertical co-efficient, d= diagonal co-efficient Energy functions (mean) are calculated using equations (1) to (2): (1) 25
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME (2) 3.5. Classification Model The features are stored for each 15 image with different expressions of 20 different persons. The classification is carried out using only one type of feature set that consists of all 24 features ie 2 features from each 12 blocks of the image. The output layer consists of 20 nodes represented in binary digits. The output is given in Table 1 for recognizing face. Table 1. Output Pattern for Recognition Person Person Output Pattern Person 1 10000000000000000000 Person 11 00000000001000000000 Person 2 01000000000000000000 Person 12 00000000000100000000 Person 3 00100000000000000000 Person 13 00000000000010000000 Person 4 00010000000000000000 Person 14 00000000000001000000 Person 5 00001000000000000000 Person 15 00000000000000100000 Person 6 00000100000000000000 Person 16 00000000000000010000 Person 7 00000010000000000000 Person 17 00000000000000001000 Person 8 00000001000000000000 Person 18 00000000000000000100 Person 9 00000000100000000000 Person 19 00000000000000000010 Person 10 4. Output Pattern 00000000010000000000 Person 20 00000000000000000001 EXPERIMENTAL RESULTS AND ANALYSIS Face images of 20 different people with 20 different expressions are collected. Database consists of frontal face images and with same background. 4.1. An Experimental Analysis for a Sample Face Image Each image is of size 200*180. The original image is converted into gray scale image as shown in Fig 3. Fig. 3. a) A Sample Face Test Image b) Gray Image Each image is divided into 12 blocks each of size 50*60. For each block, Discrete Wavelet Transform is applied. It computes approximation coefficients matrix and details coefficients matrices (horizontal, vertical, and diagonal, respectively), of each block of the image. The Fig 4 shows the 12 blocks with first level decomposition. 26
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME Block First Level Decomposition of dwt2 Fig 4. The 12 blocks with first level decomposition 27
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME The Table 2 shows the recorded results of 5 persons with 5 different expressions. TABLE 2. The recorded results of 5 persons with 5 different expressions 28
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME 4.2. An Experimental Analysis dealing with various issues Our database consists of total 400 images out of those 300 images have been used to train the neural network and 100 images have been used for testing against the trained images, and the following analysis have been obtained. From 300 trained images 297 images are correctly matched only 3 images match is not found. From 100 testing images 90 images have been perfectly matched. i.e; 10 images matches are not found. Out for 400 total of 13 where mismatched, we have obtained an accuracy of 96.75%. The overall performance of the system after conducting the experimentation on the dataset is reported in Table 3. Classifier Neural Network 5. TABLE 3. Overall System Performance Total images Misclassification Accuracy (in %) 400 13 96.75 CONCLUSION Finally we reach with conclusion. In this project we have designed one of the best approaches to recognize the faces. This method uses wavelet transform for extracting feature vectors. From the experimental results, it is seen that this method gets the best results compared to the other face recognition methods, which are supposed to be the most successive ones. This technique is not only computationally less extensive as compared with other recognition techniques but also provides best recognition result of 96.75% on images various constraints like sad, happy, sleepy, surprise, open/closed eyes, smiling and non smiling face. Several open questions still remain in our face recognition system. The robustness for image variation in rotations, illumination, etc. must be improved. Here, we evaluated the recognition performance only for small database from the aspect of security systems, such simple evaluations are less useful. Hence, the evaluation on the robustness for the largest data sets is necessary in practical use. REFERENCES       Gordon, Gaile G; Vincent, Lue M., Application of morphology to feature extraction for face recognition Proc. SPIE Vol. 1658, P.151-164 Nonlinear Image Processing III, Edwared R. Dougherty; Jaako T. Astola; Charles G. Boncelet; Eds. Rojana Kam art and Thanpant Raicharoen, “Facial recognition using feature extraction based on descriptive statistics of face image” 2009. Zhang Baizhen Ruan Qiuqi, “Facial recognition using improved deformable templates” Vol.4 issue date 16-20 2006. Y. Pang et al., “Gabor based region covariance matrices for face recognition” IEEE Transaction on circuit systems for video technology, vol. 18, no, 7, 2008. S. Sharavanan and M. Azath, “LDA Based face recognition by using hidden markov model in current trends” International journal of engineering and technology Vol. 1(2), 2009, 77-85. Sarawat Anam, Md, Shjohidul Islam, M. A. Kashem, M.N.Islam, M.R. Islam, M.S.Islam “Facial recognition using Genetic Algorithm and Back Propagation Neural Network” Proceeding of the International Multi Conference of Engineers and computer Scientists 2009 Vol I IMECS 2009, March 18-20, 2009, Hong Kong. 29
International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME               Dewi Agushinta R.l, Adang Suhendra 2, Yuhilza Hanum “Facial features Distance Extraction as a facial recognition system component”. Ching liang su “Facial recognition using Feature Orientation and Feature Geometry Matching “ Vol. 28, issue 1-2 ISSN: 0921-0296, Issue date 30 June 2004. Fei Zuo, Eindhoven “Fast Facial Feature Extraction Using Deformable Shape Model With Haar Wavled Based Local Texture Attributes.” Vol.2. Hima Deepthi Vankayalapati and Kyandogere Kyamakya “Nonlinear Feature Extraction Approaches with Application to Face Recognition over Large Databases”. Gilbert Strang and Truong Nguyen, “Wavelets and Filter Banks” WellesleyCambridge Press 1996. Burrus C.S. Gopinath R.A., Guo H, “Introduction to Wavelets and Wavelets Transforms” A Primer. Prentice-Hall 1998. Charles K. Chui, “An Introduction to Wavelets” Academics Press 1992. Chan A.K. and Liu S.J., Wavelet Tool ware: Software for Wavelet Training. Academics Press 1998. U.K. Jaliya and J.M. Rathod, “A Survey on Human Face Recognition Invariant to Illumination”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 517 - 525, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Sambhunath Biswas and Amrita Biswas, “Fourier Mellin Transform Based Face Recognition”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 8 - 15, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. A.Hemlata and Mahesh Motwani, “Single Frontal Face Detection by Finding Dark Pixel Group and Comparing XY-Value of Facial Features”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 471 - 481, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Prof. B.S Patil and Prof. A.R Yardi, “Real Time Face Recognition System using Eigen Faces”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 72 - 79, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. Archana H. Sable and Dr. Girish V. Chowdhary, “A Two Phase Algorithm for Face Recognition in Frequency Domain”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 6, 2013, pp. 127 - 135, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Jyoti Verma and Vineet Richariya, “Face Detection and Recognition Model Based on Skin Colour and Edge Information for Frontal Face Images”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 384 - 393, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 30
Email sent successfully..