A Hybrid Approach for Human Face Recognition System

Transcription

1 A Hybrid Approach for Human Face Recognition System Pooja Sharma Kamal Kumar Sharma Sharad Chouhan M. Tech Scholar, Professor Assistant Professor Department of CSE Department of ECE Department of CSE E-Max group of Institutions E-Max group of Institutions E-Max group of Institutions Ambala, India. Ambala, India. Ambala, India. Abstract: Biometrics recognition systems are originated from real life criminal and forensic applications. Some methods such as finger prints and face recognition already proved to be very efficient in human recognition.human face detection and recognition play important roles in many applications such as video surveillance and face image database management. In our proposed approach we have given the concept of hybrid face recognition using ant colony optimization with fuzzy logic. Here we have used Ant Colony Optimization for computation of features. ANT algorithm is metaheuristic used to solve combinatorial optimization problem. ANT algorithm often shows good optimization behavior. We have considered pheromone table which is generated by inputting human face in our proposed model. We have considered frontal face of human for recognition. We have computed two parameters that are Mean above Threshold (MAT) and Mean below Threshold (MBT) from pheromone matrix. These two values have been used with fuzzy logic to achieved better classification. We have done two testing, with fuzzy and without fuzzy logic. In first testing we get average accuracy of 80.95% and in second testing we achieved accuracy of 85.71%. It is concluded that Ant Colony Optimization with Fuzzy Logic gives better classification rate. Keyword: Face Recognition, Ant Colony Optimization, Fuzzy Logic I. INTRODUCTION Automated face recognition is an interesting computer vision problem with many commercial and law enforcement applications. While research into this area dates back to the 1960's, it is only very recently that acceptable results have been obtained. However, face Recognition is still an area of active research since a completely successful approach or model has not been proposed to solve the face recognition problem. Over the last few decades many techniques have been proposed for face recognition. Many of the techniques proposed during the early stages of computer vision cannot be considered successful, but almost all of the recent approaches to the face recognition problem have been creditable. Human face recognition can be divided into two strategies: (1) Geometrical features and (2) Template matching. Face Recognition Using Geometrical Features: This technique involves computation of a set of geometrical features such as nose width and length, mouth position and chin shape, etc. from the picture of the face we want to recognize. This set of features is then matched with the features of known individuals. A suitable metric such as Euclidean distance (finding the closest vector) can be used to find the closest match. Most pioneering work in face recognition was done using geometric features Kanade, [12], although Craw et al. [1] did relatively recent work in this area. The advantage of using geometrical features as a basis for face recognition is that recognition is possible even at very low resolutions and with noisy images Face Recognition Using Template Matching: This is similar the template matching technique used in face detection, except here we are not trying to classify an image as a 'face' or 'non-face' but are trying to recognize a face. The basis of the template matching strategy is to extract whole facial regions (matrix of pixels) and compare these with the stored images of known individuals. Once again Euclidean distance can be used to

2 find the closest match. The simple technique of comparing grey-scale intensity values for face recognition was used by Baron [4]. In this research work we have given a hybrid approach using ant colony optimization and fuzzy logic for face recognition. In computer science and operations research, the Ant Colony Optimization algorithm (ACO) is a probabilistic technique for solving computational problems. Ant Colony Optimization (ACO) is a new natural computation algorithm from mimic the behaviors of ants colony and proposed by Italy Scholor M. Dorigo in 1990 s [14]. II. PROPOSED WORK The main contribution of this research work is to provide an approach for frontal-view human face recognition systems based on ant colony optimization with fuzzy. Emphasis is even to the different computational and mathematical models that were modified by the researcher to satisfy these specific problems. We present an ACO based with fuzzy logic for face recognition. The algorithm is based on the pheromone table. The architecture of our proposed work is shown in Figure 1. of our proposed work. The figure 2 shows how an image is inputted in the proposed system. Fig2. Shows how an image is inputted in the proposed system. After an image is inputted, we apply ant colony optimization concept proposed by[14]. After applying ACO a pheromone values τ i,j are generated, is define as follows: m k τ i,j = (1 ρ)τ i.j + ρ τ i,j τ k i,j k=1 = 1 L k Where m defines member of ants and L k defines the length of tour created by ant k. An example of a pheromone matrix for an image is shown in Figure 3 Fig 1. Block Architecture of Proposed work. Our proposed work is divided into two blocks that is preprocessing and post processing. In the first block we have three modules that is input biometric face image, apply ACO and Pheromone matrix generation. The first module, input image is used to input images for analysis Fig3 shows a pheromone matrix of a particular image. After having the pheromone matrix, then this pheromone matrix is inputted in a second phase that post processing. Then a threshold value has been chosen [17] to find the average intensity value of the each pixel. Here the pheromone matrix τ (N) is divided into two classes using thresholdt (l). The first class is the Mean Above threshold

3 (MAT) and Mean Below threshold (MBT) which is calculated by [5]. MAT and MBT values of Face images that are used for analysis are shown in table 1 The following figure 4 as showed how rules are created in the FIS toolbox. Table 1. Shows MAT and MBT values Face Images used for analysis Subjects MAT MBT After computing MAT and MBT, these two values are inputted into FIS for finding the optimized result. FIS compare and produces result with the database values according to the following fuzzy rules: if (ACO 1 is Yes) and (ACO 2 is Yes) Then (match is Excellent) (1) if (ACO 1 is No) and (ACO 2 is Yes) Then (match is Average) (1) if (ACO is Yes) and (ACO is No) Then (match is Average) (1) if (ACO1 is No) and (ACO 2 is No) Then (match is No match) (1) if (ACO 1 is No) and (ACO 2 is No) Then (match is No match) (0) if (ACO 1 is Yes) and (ACO 2 is Yes) Then (match is excellent) (0) if (ACO 1 is No) and (ACO 2 is Yes) Then (match is Average) (0) if (ACO 1 is Yes) and (ACO 2 is No) Then (match is Average) (0) Fig 4 shows the rules design in FIS. In the above fuzzy rules ACO1 represents MAT and ACO2 represents MBT. As the proposed model depicts that when a particular Face image is inputted in our proposed system, the output of the fuzzy module is stored in database and for matching analysis. This whole process is repeated for further subject face and testing images for analysis. In our proposed work we have considered 21 frontal face images, and eight are testing images. III. thirteen images are original images PROPOSED ALGORITHM 1. Input Frontal Face Image of Subject 2. Apply ACO on input image. 3. Compute Pheromone matrix of an inputted image m k τ i,j = (1 ρ)τ i.j + ρ τ i,j k=1 4. Compute two mean values MAT (mean above threshold) and MBT (mean below threshold). 5. Input MAT and MBT into fuzzy inference system. 6. Stored output data from fuzzy into database. 7. Input query image and apply steps 2 to 5 on input query image. 8. Match query image with stored images at step Desired result after step

4 IV. PROPOSED FLOWCHART Fig7. Result of first Analysis CCR. Fig 5. Shows the Flowchart of Proposed work. V. RESULT ANALYSIS Our algorithm is implemented on Matlab R2011a.We has taken 21 frontal face images for analysis of our proposed work. Analysis of our proposed work is divided into two phases. Phase I: Without fuzzy Phase II: With fuzzy Phase I Analysis: We perform analysis on two parameters i. e. MAT (Mean above Threshold) and MBT (Mean Below Threshold) values of 21 subjects shown in table 1, to find correct classification rate (CCR). Figure6 shows the two Parameters variations on 21 subjects From the first phase of analysis it is concluded that parameter MBT(Mean below threshold) have better correct classification rate of 85.71% and MAT(Mean above threshold have correct classification rate of 76.19%. the average classification rate achieved is 80.95%. Phase II Analysis: The second phase of result analysis is based on analysis with fuzzy classifier. in this phase of analysis two parameters MAT(Mean above threshold) and MBT(Mean below threshold) is passed into the fuzzy classifier to achieved better recognition rate. here 8 subjects have been used for testing with stored 13 subject's values. In fuzzy classifier we have considered Mamdani member function. Output of the fuzzy classifier is shown in table 3. Table 3. Output of fuzzy classifier. ' Fig6. Shows variation of 21 subjects MAT & MBT. Table2. Result of first analysis, without fuzzy classifier. Parameter CCR(Correct Classification Rate) MAT MBT From figure 8 depicts that there is 3 False Acceptances in database. So the Excellent False Match Rate is 14.28% and Excellent Match Rate is 85.71%

5 76.19% and second parameter MBT (Mean Above Threshold) achieved accuracy of 85.71%. In second phase of testing, we have done analysis of matching of original images with testing subjects using Fuzzy Logic. The table 5.3 shows the results of second phase testing, which gives an efficiency of 85.71%. it is concluded from analysis that ACO with Fuzzy give better classification rate. REFERENCES Fig8. Shows the result of fuzzy classifier with testing subjects. From both phase of analysis we conclude that face recognition with Ant Colony Optimization with fuzzy classifier gives better output as compared to first phase testing. Table 4. Comparison of two testing Testing Correct Classification Rate First Analysis 80.95% Second Analysis 85.71% Fig9. Shows the comparison result of two analysis. VI. CONCLUSION In our research work, we have given an approach for Frontal Face matching using ACO with Rule Based Fuzzy System for getting better Face Recognition Rate. In our work we have computed two values MAT and MBT from a particular face. Then these values are inputted in the fuzzy system to get better matching rate. we have done two types of testing for recognition of subjects. In first testing we have done analysis without fuzzy logic. The table 5.1 shows result of first analysis and which gives an average efficiency of 80.95%.in first analysis first parameter MAT (Mean above threshold) gives accuracy of [1] Craw, I., Ellis, H., and Lishman, J.R. (1987). Automatic extraction of face features. Pattern Recognition Letters, 5: , February. [2] Sergey Subbotin and Alexey Oleynik, Modifications of Ant Colony Optimization Method for Feature Selection, CADSM 2007, February 20-24, 2007, Polyana, UKRAINE [3] Wei Gao, Study on Immunized Ant Colony Optimization, Third International Conference on Natural Computation (ICNC 2007). [4] Baron, R. J. (1981). Mechanisms of human facial recognition. International Journal of Man Machine Studies, 15: [5] Xiaoyong Liu, Guangzhou, Guangdong, Hui Fu, An Effective Clustering Algorithm With Ant Colony, Journal of Computers, Vol. 5, No. 4, April 2010 [6] Daniel Cabrini Hauagge Noah Snavely, Image Matching using Local Symmetry Features, Cornell University [7] Simranjeet Kaur, Prateek Agarwal, Rajbir Singh Rana, Ant Colony Optimization :A Technique used for Image Processing, IJCST Vol. 2, Iss ue 2, June [8] Prahlad Vadakkepat, Peter Lim,Liyanage C. De Silva,Liu Jing, and Li Li Ling, Multimodal Approach to Human-Face Detection and Tracking, IEEE Transactions On Industrial Electronics, Vol. 55, No. 3, March [9] Haiyuan Wu, Qian Chen, and Masahiko Yachida, Face Detection From Color Images Using a Fuzzy Pattern Matching Method, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 21, No. 6, June [10] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing Prentice Hall, Processing, Volume 2 nd, May [11] Weili JIAO, Yaling FANG, Guojin, An Integrated Feature Based Method for sub-pixel Image matching, the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1., Beijing , CHINA, [12] Kanade, T. (1973) Picture processing by computer complex and recognition of human faces. Technical report, Kyoto University, Dept. of Information Science. [13] M. Dorigo, V. Maniezzo, and A. Colorni, Positive feedback as a search strategy, Tech. Report , Dipartimento di Elettronica, Politecnico di Milano, Italy, [14] M. Dorigo, Optimization, learning and natural algorithms (in Italian), Ph.D. Thesis, Dipartimento dielettronica, Politecnico di Milano, Italy,

6 [15] A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian. Ant System for Job-shop Scheduling. Belgian Journal of Operations Research, Statistics and Computer Sci-ence, 34(1):39-53, [17] S. Goss, S. Aron, J.-L. Deneubourg, and J. M. Pasteels, Self- Organized Shortcuts in the Argentine Ant, Naturwissenchaften, Vol. 76, pp , [18] L.A. Zadeh, Fuzzy Sets, Information and Control, 1965 [19] L.A. Zadeh, Outline of A New Approach to the Analysis of of Complex Systems and Decision Processes, [20] M. Hellmann, Fuzzy Logic Introduction,Laboratoire Antennes Radar Telecom, F.R.E CNRS 2272, Equipe Radar Polarimetrie, [21] N. Xiong, J. He, Y. Yang, Y. He, T. Kim, and C. Lin, A Survey on Decentralized Flocking Schemes for a Set of Autonomous Mobile Robots (Invited Paper). Journal of Communications, Vol. 5, No. 1, pp , [22]. Neuro Fuzzy Systems by Lamba, V.K.First Edition,2008. PP 22-24,47-52,58,79,85. [23] M. Dorigo and G. Di Caro. New Ideas in Optimisation. McGraw Hill, London, UK, [24] C. Garcia, G. Tziritas. Face detection using quantized skin color regions merging and wavelet packet analysis, IEEE Trans. Multimedia, vol. 1, no. 3, pp ,