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

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SPORTSMANSHIPThe Independence School District is a member of the Greater Kansas City Suburban Conferenceand Missouri State High School Activities Association (MSHSAA). As members of thesegroups, we adhere to policies set down for the display of good sportsmanship by athletes,students, fans, parents and patrons. Member schools are to enforce sportsmanship rules for ourown school, players and spectators. Violation of rules can result in the following consequences:written reprimand, probation, suspension, ejection or permanent removal from events.Consequences are in force for players, coaches, students, parents and fans.All school discipline policies and rules apply to the Independence School District students atschool activities whether they are held on school property or away from school.GOOD SPORTSMANSHIP CODEFor Players, Coaches and Fans1. Maintain pride in self and school.2. Strive to keep high standards of conduct.3. Cheering is always encouraged for one’s own team.4. No taunts, chants, noises, cheers, songs, profanity, signs or motionsdirected to the opposing team, coach, school or officials. Treat everyone with respect.5. Positive signs may be displayed for one’s own team.6. No disrespect will be shown to the opposing team during introductions.7. No noisemakers.8. No continuous standing.9. Abide by the decisions of officials.10. Accept victory or defeat graciously.The Independence School District does designate a student spirit section where standing ispermitted.The Independence School District grounds (inside and outside) are, by the Independence SchoolDistrict Board policy, a tobacco-free environment. There is to be no smoking inside the buildingor outside on any school or athletic grounds.4
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-
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