International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
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40120140506007

  1. 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 53 COMPARISON OF THE PERFORMANCE OF EIGNFACE AND FISHERFACE ALGORITHM Girish D. Bonde Assistant Prof, Department of E&TC J.T.Mahajan College of Engineering, Faizpur, Maharashtra, INDIA O.K.Firke Assistant Prof, Department of E&TC J.T.Mahajan College of Engineering, Faizpur, Maharashtra, INDIA G.L.Attarde Assistant Prof, Department of E&TC J.T.Mahajan College of Engineering, Faizpur, Maharashtra, INDIA ABSTRACT In this paper, we implemented eigenface based face recognition and tried to compare the results with fisherface algorithm. The process required preprocessing. The images had to be resized to a consistent size. The database used included cropped faces of various sizes. Hence the need for face detection was eliminated. We tried to compare two of the most frequently used algorithms; Eigenface and Fisherface. We compared the performance of each algorithm against two constraints. Pose and the size of training data. Testing dense sparse database with both algorithms. The performance of Eigenface is 100% and 70% with respectively dense and sparse database. The performance of Fisherface is 80% with sparse database. The effectiveness of Fisherface across pose is good, even with limited data and Eigenface across pose is some with enough data. Our study has shown us that Fisherface algorithm is robust in both cases. This leads us conclude that the Eigenface algorithm is beneficial when the database is large. But given the robustness of the Fisherface algorithm, it would be the algorithm of choice if the resources are not a problem. Keywords: Eigenface; Fisherface INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME: http://www.iaeme.com/IJECET.asp Journal Impact Factor (2014): 7.2836 (Calculated by GISI) www.jifactor.com IJECET © I A E M E
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 54 I. INTRODUCTION The face plays a major role in our social intercourse in conveying identity and emotion. The human ability to recognize faces is remarkable. We can recognize thousands of faces learned throughout our lifetime and recognize the faces at a glance even after few years. The skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses or changes in hairstyle. We have implemented the eigenface and fisherface algorithms and tested them against two face databases, observing results across pose (out-of-plane face rotation). We evaluated performance against databases with both densely-sampled and sparsely-sampled facial poses.We have also extended the work towards automatic estimation of pose parameters. Given a training database of pre-processed face images, train an automated system to recognize the identity of a person from a new image of the person. Examine sensitivity to pose using the eigenface approach suggested in [1,2] and the fisherface approach developed in [3]. 1. Comparing results of eigenface & fisherface across pose. 2. Testing dense and sparse training databases. II. RELATED WORK The Eigenface is the first method considered as a successful technique of face recognition [1,2,11]. The Eigenface method uses Principal Component Analysis (PCA) to linearly project the image space to a low dimensional feature space. The Fisherface is an enhancement of the Eigenface method [3,8]. The Eigenface method uses PCA for dimensionality reduction, thus, yields projection directions that maximize the total scatter across all classes, i.e., across all image s of all faces. The PCA projections are optimal for representation in a low dimensional basis, but they may not be optional from a discrimination standpoint. Instead, the Fisherface method uses Fisher’s Linear Discriminant Analysis (FLDA or LDA) which maximizes the ratio of between-class scatter to that of within-class scatter. III. COMPARISION BETWEEN EIGENFACE AND FISHERFACE Eigenface and Fisherface are global approach for face recognition takes entire image for a 2- D array of pixels. Both methods are quite similar as Fisherface is a modified version of Eigenface [4]. Both make use of linear projection of the images into a face space, which take the common features of face and find a suitable orthonormal basis for the projection. The difference between them is the method of projection is different; Eigenface uses PCA while Fisherface uses FLD. PCA works better with dimension reduction and FLD works better for classification of different classes. A. Eigenface Eigenface is a practical approach for face recognition. Due to the simplicity of its algorithm, we could implement an Eigenface recognition system easily. Besides, it is efficient in processing time and storage. PCA reduces the dimension size of an image greatly in a short period of time. The accuracy of Eigenface is also satisfactory (over 90 %) with frontal faces[1]. However, as there has a high correlation between the training data and the recognition data. B. Fisherface Fisherface is similar to Eigenface but with improvement in better classification of different classes image. With FLD, we could classify the training set to deal with different people and different pose. We could have better accuracy in various pose than Eigenface approach. Besides,
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 55 Fisherface removes the first three principal components which is responsible for light intensity changes, it is more invariant to light intensity. Fisherface is more complex than Eigenface in finding the projection of face space. Calculation of ratio of between-class scatter to within-class scatter requires a lot of processing time. Besides, due to the need of better classification, the dimension of projection in face space is not as compact as Eigenface, results in larger storage of the face and more processing time in recognition. Facial recognition software was developed using the MATLAB programming language by the MathWorks. This environment was chosen because it easily supports image processing, image visualization, and linear algebra. The software was tested against UMIST database. UMIST was created by Daniel B. Graham, with a purpose of collecting a controlled set of images that vary pose uniformly from frontal to side view. The UMIST database has 565 total images of 20 people. The UMIST database images, displayed below, has uniform lighting and pose varying from side to frontal. Figure 1: UMIST database Images C. Comparison by Size of training data For these results, 20 recognition faces (one for each person) were randomly picked from the database, leaving 545 photos to use as training faces. Mp, the number of principal components to use, was chosen as 20. All 20 of 20 images were correctly recognized, the result is 100%, confirming the very good performance of eigenface with densely and uniformly sampled inputs. For this same database and setup, fisherface performs very similarly.
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 56 Figure 2: UMIST Results Using Eigenfaces in Densely Sampled Database D. Comparison by Image pose For these results, 20 recognition faces (one for each person) were randomly picked from the database, then 60 more photos were used as training faces. Three training faces were picked for each person: a frontal, side, and 45-degree view[4].
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 57 Figure 3: UMIST Results Using Fisherface in Sparsely Sampled Database.
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 58 Out of the 20 faces, 16 were correctly classified in the 1st match. Also notice that this approach is rather pose invariant. it often (13 times) picks out all 3 training images from the database[4]. Figure 4 : UMIST Results Using Eigenface in Sparsely Sampled Database.
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 59 For comparison, the same setup is run using the eigenface algorithm. Here 14 of the 20 faces are correctly classified, and all 3 correct images are never found. Clearly, the fisherface algorithm performs better under pose variation when only a few samples across pose are available in the training set. TABLE I. COMPARING EIGENFACE AND FISHERFACE Fisherface Eigenface Computational Complexity Slightly more complex Simple Effectiveness Across Pose Good, even with limited data Some, with enough data Sensitivity to Lighting Little Very We find that both the Eigenface and Fisherface techniques work very well for a uniformly and densely sampled data set varied over pose. When a more sparse data set across pose is available, the fisherface approach performs better than Eigenface[4]. IV. CONCLUSION AND FUTURE WORK The Eigenface and Fisherface method were investigated and compared. The comparative experiment showed that the Fisherface method outperformed the Eigenface method. The usefulness of the Fisherface method under varying pose and varying sizes of training databases was verified. Also our results show that patch-based representation is suitable for face pose estimation. REFERENCES 1. M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, 1991. 2. M. Turk and A. Pentland, “Face recognition using eigenfaces,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991, pp. 586-591. 3. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997. 4. Abhishek Choubey and Girish D. Bonde, “Face Recognition Across Pose withEstimation of Pose Parameters” International journal of Electronics and CommunicationEngineering &Technology (IJECET), Volume 3, Issue 1, 2012, pp. 311 - 316, ISSN Print:0976- 6464, ISSN Online: 0976 –6472 5. Alan Brooks (in collaboration with Li Gao) Face Recognition: Eigenface and Fisherface Performance Across Pose, ECE 432 Computer Vision with Professor Ying Wu 2004 6. G. Little, Krishna S., Black J., and Panchanathan S. A methodology for evaluating robustness of face recognition algorithms with respect to changes in pose and illumination angle. In ICASSP, 2005 7. Belhuumeur, P. N., Hespanha, J. P., and Kriegman, D.J. 1997. Eigenfaces vs. Fisherfaces: Recognitionusing class specific linear projection. IEEETrans. Patt. Anal. Mach. Intell. 19, 711–720. 8. Huang, J., Heisele, B., and Blanz, V. 2003. Component-based face recognition with 3D morphable models. In Proceedings, International Conference on Audio- and Video-Based Person Authentication. 9. Lanitis, A., Taylor, C. J., and Cootes, T. F. 1995. Automatic face identification system using flexible appearance models. Image Vis. Comput. 13, 393–401
  8. 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME 60 10. Marian Stewart Bartlett, Javier R. Movellan and Terrence J. Sejnowski,2002.Face Recognition by Independent Component analysis. IEEE Transactions on Neural Networks , Vol. 13, No. 6. 11. Turk, M. and Petntland, A. 1991. Eigenfaces for recognition. J. Cogn. Neurosci. 3, 72–86. 12. Zaho W., R.Chellepa, A.Rosenfeld, 2003. Face Recognition: A Literature Survey, ACM Computing Surveys, Vol.35, No.4, pp.399-458 13. Prof. B.S Patil and Prof. A.R Yardi, “Real Time Face Recognition System Using Eigen Faces” International journal of Electronics and CommunicationEngineering &Technology (IJECET), Volume 4, Issue 2, 2013, pp. 72 - 79, ISSN Print:0976- 6464, ISSN Online: 0976 –6472 14. Mrs. Manisha Bhisekar and Prof. Prajakta Deshmane, “Image Retrieval and Face Recognition Techniques: Literature Survey” International journal of Electronics and Communication Engineering &Technology (IJECET), Volume 5, Issue 1, 2014, pp. 52 - 58, ISSN Print:0976- 6464, ISSN Online: 0976 –6472 15. J. V. Gorabal and Manjaiah D. H., “Texture Analysis For Face Recognition” International Journal Of Graphics And Multimedia (IJGM), Volume 4, Issue 2, 2013, pp. 20 - 30, ISSN 0976 - 6448 (Print), ISSN 0976 -6456 (Online) 16. Bilal Salih Abed Alhayani and Prof. Milind Rane, “Face Recognition System by Image Processing” International journal of Electronics and Communication Engineering &Technology (IJECET), Volume 5, Issue 5, 2014, pp. 80 - 90, ISSN Print:0976- 6464, ISSN Online: 0976 –6472

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