A Comparison of Histogram and Template Matching for Face ...

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Rotation Image TM Histogramin degrees−20 Figure 5(a) 0.8893 0.9750−10 Figure 5(b) 0.9235 0.9935−5 Figure 5(c) 0.9455 0.99750 Figure 1 1.0000 1.0000+5 Figure 5(d) 0.9506 0.9992+10 Figure 5(e) 0.9243 0.9945+20 Figure 5(f) 0.8847 0.9574Table 5. Similarity values of rotated face images.Translation TM Histogram Variation inin pixelsSimilarity6 (X) 0.9598 0.9913 3.3%4 (X) 0.9677 0.9963 3.0%2 (X) 0.9760 0.9965 2.1%0 1.0000 1.0000 0.0%2 (Y) 0.9790 0.9992 2.1%4 (Y) 0.9662 0.9966 3.1%6 (Y) 0.9565 0.9879 3.3%Table 6. Similarity values of translated faceimages (X and Y directions).(a) (b) (c)Figure 6. Sample translated images in X andY directions (shown by dark lines).(d) (e) (f)Figure 5. Sample rotated images.A global assessment of all experiments is shown in Table7, where it can be seen that for images that involve geometrictransformations, HM is the best method, and it isalso suitable for images with Gaussian blur. Since the averagevariation in similarity values between HM and TM forGaussian blur is about 1.0%, it can rougly be concluded thatboth methods are suitable for this disturbing factor. The previousconclusion regarding HM confirms that histogramsare invariant to rotation and translation, as mentioned inSection 3. At the same time, TM produces the best performancewhen dealing with RGB noise and different lightingconditions. As shown in Table 7, the average variation ofsimilarity values is most significant for changes in lightingconditions when compared to other image variations. Figures7 and 8 summarize the brief discussion in this section.These graphs were plotted using the variation in similarityvalues between TM and HM for each image – the graphin Figure 7 regards images with added noise and changes inlighting, and the graph in Figure 8 regards images with ge-ometric transformations. From these graphs, it can be easilyseen that different lighting conditions and rotation resultin significant similarity variations between TM and HM.Image Best Average VariationVariation Method in SimilarityGaussian blur Histogram 1.0%RGB noise TM 6.5%Lighting TM 20.7%Scaling Histogram 2.7%Rotation Histogram 6.2%Translation Histogram 2.4%6. ConclusionTable 7. Performance comparison.In this work, TM based on cross-correlation and histogramswere used to compare face images. In real-worldapplications, images may have variations due to noise, lightingconditions, scaling, rotation and translation. To understandand analyze the influence of image variations in theface verification process, TM and HM methods were compared.Both methods are dependent on the value of im-

color distribution and are suitable for face recognition andrelated tasks, when dealing with image influenced by disturbingfactors more investigation using local image informationis needed.ReferencesFigure 7. Variation in similarity values betweenTM and histogram for Gaussian blur,RGB noise and illumination variation.Figure 8. Variation in similarity values betweenTM and histogram for scaling, rotationand translation.age pixels – TM depends on the local pixel information,mean HM on the global pixel information of the face images.According to the comparison of methods applied tothe face object image and different target face images usedin this work, TM can be considered as a suitable methodfor images with RGB noise, Gaussian blur and images withslight variations in lighting conditions, and HM for face imagesunder different geometric transformations. As a generalconclusion, it can be pointed out that images withchanges in illumination require more investigation so thatthe most suitable matching method for face verificationcan be determined. In this work, global histograms of theRGB color channels were analyzed for face verification. Althoughglobal histograms capture and represent the image[1] J. R. Beveridge, G. H. Givens, P. J. Philips, B. A. Draper, andY. M. Lui. Focus on quality, predicting FRVT 2006 performance.In Proceedings of the 8th IEEE International Conferenceon Automatic Face and Gesture Recognition, pages1–8, 2008.[2] G. Bradski and A. Kaehler. Learning OpenCV. O’Reilly Media,2008.[3] R. Brunelli and T. Poggio. Template matching: Matched spatialfilters and beyond. Pattern Recognition, 30(5):751–768,May 1997.[4] G. D. Finlayson, S. S. Chatterjee, and B. V. Funt. Color angularindexing. In Proceedings of the 4th European Conferencein Computer Vision, pages 16–27, 1996.[5] R. C. Gonzalez and R. E. Woods. Digital Image Processing.Prentice Hall, 3rd edition, 2009.[6] H. Guo, Y. Yu, and Q. Jia. Face detection with abstracttemplate. In Proceedings of the 3rd International Congresson Image and Signal Processing, volume 1, pages 129–134,2010.[7] W. Jia, H. Zhang, X. He, and Q. Wu. A comparison on histogrambased image matching methods. In Proceedings ofthe 3rd IEEE International Conference on Video and SignalBased Surveillance, pages 97–102, 2006.[8] Z. Jin, Z. Lou, J. Yang, and Q. Sun. Face detection usingtemplate matching and skin-color information. Neurocomputing,70(4-6):794–800, January 2007.[9] Z. Liu and C. Liu. A hybrid color and frequency featuresmethod for face recognition. IEEE Transactions on ImageProcessing, 17(10):1975–1980, October 2008.[10] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada.Color and texture descriptors. IEEE Transactions onCircuits and Systems for Video Technology, 11(6):703–715,June 2001.[11] S. E. Palmer. Vision Science: Photons to Phenomenology.MIT Press, 1999.[12] G. Pass and R. Zabih. Comparing images using joint histograms.Multimedia Systems, 7(3):234–240, 1999.[13] A. K. Sao and B. Yegnanarayana. Face verification usingtemplate matching. IEEE Transactions on InformationForensics and Security, 2(3):636–641, September 2007.[14] X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Face recognitionfrom a single image per person: A survey. Pattern Recognition,39(9):1725–1745, September 2006.[15] M.-H. Yang, D. J. Kriegman, and N. Ahuja. Detecting facesin images: A survey. IEEE Transactions on Pattern Recognitionand Machine Intelligence, 21(1):34–58, January 2002.[16] H. Zhou and G. Schaefer. Semantic features for face recognition.In Proceedings of the 52nd International SymposiumELMAR-2010, pages 33–36, 2010.