NEURAL NETWORK & GENETIC ALGORITHM BASED FACE RECOGNITION SYSTEM

Transcription

1 NEURAL NETWORK & GENETIC ALGORITHM BASED FACE RECOGNITION SYSTEM 1 Mr. Lalit Kumar P. Bhaiya, 2 Mr. Vivek Pali 1 Associate professor,hod(et&t),rcet,bhilai,lalit04_bhaiya@yahoo.com 2 M-tech scholar,rcet, Bhilai,vivekpali1986@gmail.com Abstract- Facial image analysis plays a significant role for human computer interaction. Automatic analysis of human face is still a challenging and emerging problem with many applications. In this paper, we proposed a neural network and genetic algorithm based face recognition system to recognize and verify the person from a digital image. This paper shows the advantages of using Neural network along with Genetic Algorithm for face recognition system. The neural network has the ability to adapt to unknown situations & it trains itself by learning through datasets and is fault tolerant while the Genetic algorithm is an optimization technique used in computing to find the exact or approximate solutions. It handles large & poorly understood search spaces easily and handles noisy functions well. The proposed system consists of three basic steps which automatically detect the human face image using BPNN, the feature extraction based on a fusion approach of different techniques like PCA,LDA,LBA, KDDA and face recognition will be performed based on BPNN and GA. The dimensionality of face image will be reduced by the fusion extraction block and the recognition will be done by the combined BPNN and GA for efficient and robust face recognition. Keywords- Back Propagation Neural Network(BPNN), Genetic algorithm(ga), Principal Component Analysis(PCA), Linear Discriminant Analysis(LDA), Line Based Analysis(LBA), Kernel Direct discriminant Analysis(KDDA). 1. INTRODUCTION In the field of computer vision, the face recognition has become increasingly relevant in today s scenario. Face recognition has been one of the most interesting and important research fields in the past years. Face recognition has received substantial attention from researches in biometrics, pattern recognition field, and security systems. A face recognition system recognizes an individual by matching the input image against images of all the users stored in a database and finding the best match by applying certain algorithm. Most of the face recognition approaches are based on either the overall analysis of the face image that retains the global information of the face or the location or shape of facial attributes such as the eyes, eyebrows, nose, lips and chin, and their spatial relationships or, a hybrid approach that combines above two approaches. In this paper the feature extraction is done by extracting the geometrical features of the face. For the geometrical feature extraction, the biometric characteristics of the face such as eyes, nose chin lips and mouth are considered. These extracted features form a feature vector which is the most responsible decision making element in training an artificial neural network. In this paper we proposed a hybrid model(nn+ga) for face detection and recognition, which efficiently works in a constrained environment. This hybrid model will be fast, reasonably simple and more accurate as compared to traditional face recognition systems. In this proposed model we are combining the Back propagation Neural Network (BPNN) and Genetic Algorithm. With the help of this hybrid model non linear face images can be recognized easily. In this paper the design and implementation of face recognition system is subdivided into three major steps. First step is Image pre-processing- in this step automatic face detection can be accomplished by using back propagation neural network. The second step is to extract various facial features from the facial image using a fusion approach of different techniques like PCA, LDA, LBA, KDDA. And the third step includes the artificial intelligence based face recognition which is accomplished by Back Propagation neural network (BPNN) and Genetic algorithm(ga). 2. RELATED WORK A bunch of papers have been published to overcome different problems encountered during face detection and recognition process such as illumination, expressions, scale, pose etc and achieve better recognition rate, while there is still no robust technique for the same. In order to solve such problems a number of face recognition systems have been developed which provide approximate solutions to these problems but the difficulties still remain. Face recognition system can work effectively if combined with other biometric identification techniques. Researchers have shown their chiefeditor@ijrcct.org Page 204

2 deep interest in developing a reliable and efficient face recognition system. The various systems compete with each other in reducing computation time while some for improving the recognition rate. Face recognition techniques use appearance based method in which an image of a certain size is represented by a vector in a dimensional space of size which is similar to the image.[1] However, to allow fast and robust face recognition these dimensional spaces are too large. To simplify this problem other methods were developed that uses dimensionality reduction techniques. Example of these techniques are the Principle component Analysis ( PCA)[2] and the Linear Discriminant Analysis (LDA), Kernel Direct Discriminant Analysis (KDDA)[4]. PCA is an eigen vector method designed to model linear variation in high-dimensional data. PCA performs dimensionality reduction by projection of an original n dimensional data onto a k (<<n)- dimensional linear subspace spanned by the leading eigenvectors of the data s covariance matrix. LDA is a supervised learning algorithm. In LDA, the LDP features are obtained by computing the edge response values in all eight directions at each pixel position. It generates a code from the relative strength magnitude. Each face is represented as a collection of LDP codes for face recognition process. PCA encodes information in an orthogonal linear space, while LDA encodes discriminating information in a linearly separable space and it is not necessarily orthogonal. It is generally believed that algorithms based on LDA are superior to those based on PCA. Another linear method for facial analysis is the Locality Preserving Projections (LPP) [3] in which a face subspace is obtained and the local structure of the manifold is found. LPP is a popular method for manifold learning. It is obtained by finding the optimal linear approximations to the eigen functions of the Laplace Betrami operator on the manifold. Therefore, though it is still a linear technique, it seems to recover important aspects of the intrinsic nonlinear manifold structure by preserving local structure. 3. FACTORS AFFECTING THE FACE RECOGNITION SYSTEM There are a number of factors that causes degradation in the performance of a face recognition system and that result in difficulties of face detection and face recognition. Except the possible low quality driven from image acquisition system, we focus on the angle of human faces taken by the camera and the environment of photo acquisition. Illumination Pose Expression The illumination variation has been widely discussed in many face detection and recognition researches. This variation is caused by various lighting environments and is mentioned to have larger appearance difference than the difference caused by different identities. The pose variation results from different angles and locations during the image acquisition process. This variation changes the spatial relations among facial features and causes serious distortion on the traditional appearance-based face recognition algorithms such as eigenfaces and fisherfaces. Human uses different facial expressions to express their feelings or tempers. The expression variation results in not only the spatial relation changes, but also the facial-feature shape changes RST variation The RST (rotation, scaling, and translation) variation is also caused by the variation in image acquisition process. It results in difficulties both in face detection and recognition, and may require exhaustive searching in the detection process over all possible RST parameters Cluttering Occlusion In addition to the above four variations which result in changes in facial appearances, we also need to consider the influence of environments and backgrounds around people in images. The cluttering background affects the accuracy of face detection, and face patches including this background also diminish the performance of face recognition algorithms. The occlusion is possibly the most difficult problem in face recognition and face detection. It means that some parts of human faces are unobserved, especially the facial features. There are generally six factors we need to concern: illumination, face pose, face extraction, RST (rotation, scale, and transaction) variation, clutter background, and occlusion. Table lists the details of each factor.[11,12] Table 3.1: Table shows the Factors affecting the performance of face recognition systems. 4. PROPOSED METHODOLOGY The proposed system will help to deal with above mentioned limitations of face recognition and is divided into three main phases. The first phase is image pre- chiefeditor@ijrcct.org Page 205

3 processing; the second phase is feature extraction and the third phase is face recognition. Image preprocessing is required prior to presenting the training or testing images to the neural network. The Image preprocessing can be accomplished by performing two tasks namely face image acquisition and face image enhancement. The face image acquisition can be done by scanning with help of digital cameras, webcam, scanner etc. Face image enhancement process includes noise filtering, image cropping and edge detection. The input face image may contain noisy data which must be removed before applying to the feature extractor. For fixing these problems, noise filtering can be used. After filtering, the image is clipped to remove the unnecessary background and obtain the necessary data. The edge detection is used to detect the edges of region of interest. The function of feature extraction is to perform the extraction of various facial features from a face image. This step is essential for the initialization of many face processing techniques like face tracking, facial expression recognition or face recognition. In this proposed system, the feature extraction is done by using a fusion approach of different feature extraction & reduction techniques such as Linear Discriminant Analysis (LDA), Principle Component Analysis (PCA), Kernel Direct Discriminant Analysis (KDDA) and Line Based Analysis (LBA). The purpose of feature extraction is to extract the feature vector or information to reduce the computation time and memory storage. The third part of face recognition system consists of the artificial intelligence which is composed by Genetic algorithm and back propagation neural network. Figure 4.1 shows the block diagram of proposed system. Figure 4.1: Block diagram of proposed system Firstly, face s image acquisition is achieved by web cam, digital camera or using scanner. Then image cropping is performed using start point and end point detection algorithm. Then the edges are detected using high pass filter, high boost filter, median filter or several edge detection methods. Finally, the features are extracted. These extracted features of image are then fed into genetic algorithm and Back-propagation neural network. For face recognition in this proposed method two techniques are used one is based on genetic algorithm and another one is based on Back propagation neural network. In this hybrid system, the extracted features are saved into memory and using genetic algorithm, the recognition of unknown face image is performed by comparing this special pattern to the pattern for which an image module is already built. The main advantage of this proposed model is that there is no extra learning process included, only by saving the face information of the person and appending the person s name in the learned database completes the learning process. In the second technique, extracted features are fed into the input of multilayer neural network and the network is trained to create a knowledge base for recognition which is then used for recognition. 4.1 Neural Network An artificial neural network is a non linear and adaptive mathematical module inspired by the working of a human brain. It consists of simple neuron elements operating in parallel and communicating with each other through weighted interconnections. Both NN and GA were invented in the spirit of a biological metaphor. The biological metaphor for neural networks is the human brain. Like the brain, this computing model consists of many small units that are interconnected. These units (or nodes) have very simple abilities. Hence, the power of the model derives from the interplay of these units. It depends on the structure of their connections. Of course, artificial neural networks are ages away from the dimensions and performance of the human brain. The brain consists of about 10 to 15 billion neurons with an average of about one thousand connections each. Today s artificial NN have rarely more than a few hundred nodes, most of them much less. Also, the exact structure of brain neurons is more complex than the simple computer model, it is still not completely explored. There are several different neural network models that differ widely in function and applications. They draw the most attention among researchers, especially in the GANN field. From a mathematical point of view, a feed-forward neural network is a function. It takes an input and produces an output. The input and output is represented by real numbers. A simple neural network may be illustrated like in figure chiefeditor@ijrcct.org Page 206

4 Figure 4.2: A simple Neural Network 4.2 Genetic Algorithm The genetic algorithm was introduced by Regensburg in (GAs) is a class of optimization procedures inspired by the mechanisms of natural selection [9,10]. (GAs) operates iteratively on a population of structures, each of which represents a candidate solution to the problem, encoded as a string of symbols (chromosome). A randomly generated set of such strings forms the initial population from which the (GAs) starts its search. Three basic genetic operators guide this search: selection, crossover and mutation. Figure 4.3 illustrates the principle structure of a genetic algorithm. It starts with the random generation of an initial set of individuals, the initial population. number of parameter have to be set before any training can begin. However, there are no clear rules how to set these parameters. Yet these parameters determine the success of the training.[5] By combining genetic algorithms with neural networks (GANN), the genetic algorithm is used to find these parameters. The inspiration for this idea comes from nature: In real life, the success of an individual is not only determined by his knowledge and skills, which he gained through experience (the neural network training), it also depends on his genetic heritage (set by the genetic algorithm). One might say, GANN applies a natural algorithm that proved to be very successful on this planet: It created human intelligence from scratch. There have been several systems developed that combines neural networks with genetic algorithms in various ways. These generally fall into three categories: (1) Using a GA to determine the structure of a neural network, (2) Using a GA to calculate weights of a neural network, and (3) Using a GA to both determine the structure and the weights of a neural network. In the first category, a genetic algorithm is used in an attempt to find a good structure for a neural network. The process involves the GA evolving several structures, and using the neural network as the fitness function to determine the fitness level of each structure. This technique can help eliminate the guess-work in deciding upon the structure of a neural network that can successfully be trained on the data. Figure 4.3: Principle structure of a Genetic Algorithm 4.3 Combining Neural Network & Genetic Algorithm The idea of combining GA and NN came up first in the late 80s, and it has generated a intense field of research in the 1980s. The general idea of combining GA and NN is illustrated in figure 4.4. Since both are autonomous computing methods, why combine them? In short, the problem with neural networks is that a Figure 4.4: General structure of a combined GANN In the second category, the GA is used to evolve the weights of the network rather than using back propagation or some other technique for training connection weights[6]. This can potentially result in quicker training of the network. The third category uses chiefeditor@ijrcct.org Page 207

5 the GA to generate not only the structure, but also weights in the GA. 5. RESULT The neural network and genetic algorithm are combined in order to get higher accuracy and the comparision of combination of various techniques on the basis of their percentage of accuracy is studied. A comparison of various techniques which has been used for developing a face recognition system, is shown in table 5.1 Method % of Accuracy NN with PCA 82.3 SVM with PCA+GA SVM with SFBS SVM with PCA 91.1 SVM with GA SFBS+SVM Table 5.1: Table shows the comparison of previous works 6. CONCLUSION Neural networks and Genetic algorithms are two highly popular areas of research, and integrating both techniques lead to highly successful learning systems. In this paper, face recognition based on ANN and GA is proposed. ANN with Back propagation algorithm is found to be the efficient method for recognizing the faces. The Symmetric Based Face Recognition algorithm has proved to be the best to identify and recognize the faces from a digital image efficiently. The proposed system takes less memory space, low processing time and reduces complexities. Genetic algorithm is incorporated with neural network to efficiently assign the most optimum value for the weights in the neural network. This will improve the processing speed of the face recognition system. The neural network will be trained on various ideal and noisy input images of faces repeatedly, thus making the system robust to any external disturbances. The hybrid neural network and genetic algorithm approach for face recognition is found to be more efficient than the existing digital image processing techniques. 7. REFERENCES [1] Murase, H. & Nayar, S.K, "Visual Learning and Recognition of 3-D Objects from Appearance", Journal of Computer Vision,Vol. 14, 1995, pp [2] Turk, M. & Pentland, A.P, "Face Recognition Using EigenFaces", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp [3] Niyogi, P, Locality Preserving Projections, Proceedings of Conference on Advances in Neural Information Processing Systems, [4] S.Chitra, Dr.G.Balakrishnan, A survey of face recognition on feature extraction process of dimensionality reduction techniques, Journal of Theoretical and Applied Information Technology 15th February Vol. 36 No.1 [5] Talib S. Hussain, Methods of Combining Neural Networks and Genetic Algorithms [6] C.R Vimal Chand,"Face and gender Recognition Using Genetic Algorithm and Hopfield Neural Network", Global Journal of Computer Science and Technology Vol. 10 Issue 1 (Ver 1.0), April 2010 [7] A. Blanco, M. Delgado, M.C. Pegalajar, "A genetic algorithm to obtain the optimal recurrent neural network", ELSEVIER International Journal of Approximate Reasoning 23 (2000) [8] Wolfram Schiffmann, Merten Joost, Randolf Werner: Performance Evaluation of Evolutionary Created Neural Network Topologies, in Parallel Problem Solving from Nature 2, Springer Verlag. [9] D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, [10] J. Holland, Adaptation in Natural and Artificial Systems, MIT Press, [11] Yang, M.H. ; Kriegman, D.J. & Ahuja, N. (2002). Detecting Faces in Images: A Survey, IEEE Transactions (PAMI), Vol. 24, No. 1, (2002). [12] N.Vivekanandan Reddy, D.Abhilash Krishna, P.Sharath Reddy,R.Shirisha, "Neural Network Based Intelligent Local Fac e Recognition Using Local Pattern Averaging",2011 IEEE [13] Afsaneh Alavi Naini, Fatemeh seiti,mohammad Teshnelab, Mahdi Aliyari shoorehdeli,"face Detection Based on Dimension Reduction using Probabilistic Neural Network and Genetic Algorithm", IEEE Proceeding of the 6th ISMA09, Sharjah, UAE [14]Rehmat Khan, Rohit Raja,"Neural Network associated with recognition of Facial Expressions of Basic Emotions",International Journal of Computer Trends and Technology- volume3issue Mr. Lalit Kumar P. Bhaiya is an Associate Professor & Head of Electronics & Telecommunication Department at Rungta College of Engineering & Technology, Bhilai. He did his B.E. & M.E in Electronics engineering from Amravati university (MH) in 1988 and 2004 respectively. He is pursuing his Ph.D. in Electronics Engineering from Chhattisgarh Swami Vivekanand Technical University, Bhilai. His research interest includes biomedical imaging, digital signal processing, image processing and soft computing. He has published around 42 papers in national and international journals and conferences. He is also the member of various professional societies like IETE, ISTE, IE(I). He has a vast experience of about 23 years in teaching profession. Mr. Vivek Pali is an Assistant Professor in Chhattisgarh Institute of technology, Rajnandgaon. He has passed BE in Electronics and telecommunication stream from CSIT, Durg in 2007 and currently pursuing Mtech in Digital Electronics from Rungta College of Engineering & Technology, Bhilai. chiefeditor@ijrcct.org Page 208