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Fırat Onur Alsaran, Neşe Alyüz, Melike Esma İlter, Mürsel Taşgın, Ahmet Burak Yoldemir CmpE 58Z Term Project
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Ahmet Burak Yoldemir
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Motivation Ear biometrics has several advantages over complete face Facial biometrics may fail due to: Expressions Cosmetics Hair styles Growth of facial hair Ears are affected very little from such changes
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Ear database 448 ear images are manually cropped from profile images of CMU Multi-PIE Database Only left ears are used There are 4 ear images of 112 people Illumination conditions of these 4 images are all different
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Samples from the database Person 1: Person 2: High illumination variance!
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First attempts Filter bank approaches are applied first Angular radial transform Gabor filters Leung-Malik filters Schmid filters
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First attempts Filter bank approaches are applied first Angular radial transform Gabor filters Leung-Malik filters Schmid filters
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First attempts Filter bank approaches are applied first Angular radial transform Gabor filters Leung-Malik filters Schmid filters
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First attempts Filter bank approaches are applied first Angular radial transform Gabor filters Leung-Malik filters Schmid filters
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Illumination tolerance None of the filter bank approaches is able to tolerate illumination changes, as they have fixed bases A grayscale invariant texture measure: Local Binary Patterns
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Local binary patterns - Advantages Tolerance against illumination changes Computational simplicity A compact description of the image
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Local binary patterns - Example
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Local binary patterns After obtaining LBP codes, a histogram of these codes is obtained using 256 bins This histogram is actually a histogram of micro-patterns The result is a 256 dimensional feature vector of an ear image
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Local binary patterns LBP method is very sensitive to high frequency components A slight noise can change the ordering of the pixel values in a neighborhood, which results in a different micro-pattern To prevent this, images are filtered with a Gaussian kernel of 5x5 before finding micro- patterns
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Recognition step Euclidean distance between these feature vectors is used as the (dis)similarity measure A similarity matrix is formed using these distances
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Multi-presentation approach To increase recognition performance, multi- presentation approach is adopted Each ear is represented using 2 images, verification is accomplished by taking 2 ear images of the user Mean and max rules are applied to fuse the scores
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Results – Without Gaussian filtering MethodEER (%) Original32.19 MP (max)14.73 MP(mean)1.77
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Results – With Gaussian filtering MethodEER (%) Original13.18 MP (max)5.43 MP(mean)1.14
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Mürsel Taşgın
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Facial Profile recognition Motivation Facial profile images can be collected from side cameras Computation complexity is lower Complementary solution for face recognition
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Profile Database 448 profile photos from Multi-PIE database 112 subjects, each having 4 photos Facial profiles are extracted manually in the first place
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Facial Profile Registration 12 Rotate 90º CW 3 Extract profileEdge detection 4 5 6 Scale and move to top (nose at the center) Chin & nose detection using gradient of image Nose at the center and touching top Histogram representation (image to function) gradient
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Facial Profile Registration (cont.) Edge detection(Sobel) is used to convert black-white profile image to a histogram function Profile line is decreased to a single pixel white line Nose is the highest point in the histogram Chin point is detected using gradient of histogram and image-filling function of Matlab: If gradient of the image changes sharply at chin area, it is marked as chin point If image-fill function fills in the chin area then the end point is marked as chin lips Image-filling detects lips, so use gradient to find chin
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Facial Profile Matching (Histogram Matching) Facial profiles are represented as histogram functions. After registration, pointwise distance is measured: Difference between points are summed over all points Other metrics are available as well: Bhattacharyya distance White line is profile-1 Red line is profile-2 Green vertical lines are distances
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Neşe Alyüz
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Motivation Multiple biometric sources can provide better performance Ear and Facial Profile biometrics can be acquired simultaneously Instead of using a single modality of ear or profile, apply fusion Most common fusion level: score level Heterogeneous Scores –> score normalization is important
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Score Normalization Techniques Min-max normalization Z-Score normalization Median Absolute Deviation (MAD) normalization Tanh normalization
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Min-max Normalization Best suited for the case where bounds are known Shift scores into range [0 1] Given a set of matching scores: {s k } Normalized scores: Original distribution is kept, only scaling When bounds are estimated, not robust to outliers
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Z-score Normalization Performs well if prior knowledge is available Mean and standard deviation are used Given a set of matching scores: {s k } Normalized scores: Original distribution is not retained Does not guarantee a common numerical range When mean and std are estimated, very sensitive to outliers
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Median Absolute Deviation (MAD) Normalization Median and MAD are insensitive to outliers and to points in the extreme tails of the distribution MAD normalization benefits from this fact Normalized scores: where MAD = median(|s k - median|) Median and MAD have low efficiencies When score distribution is not Gaussian, poor estimates Input distribution is not retained Normalized scores are not in a common range
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Tanh Normalization Robust to outliers Highly efficient Normalized scores: Tanh distribution: normalized genuine scores has a mean of 0.05 and std of ~o.o1. Determines the spread of genuine scores
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Score Fusion Techniques MAX rule MEAN rule SUM rule PRODUCT rule Evaluated on scores that are normalized with different approaches
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Experimental Results Initial Results on Similarity matrices of Assignment #3: Face and Fingerprint biometrics 40 subjects with 8 sample/subject SMs: 320x320 similarity matrices Enrollment: 1 sample/subject for each bimetric
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Experimental Results - EERs FusionMAXMEANSUMPRODUCT No Norm.8.588.27 14.01 Min-max14.878.64 8.32 Z-score8.157.89 20.42 MAD7.887.86 18.58 Tanh7.847.61 7.57 Individual ModalitiesEERs Face12.09 Fingerprint21.76
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Experimental Results - TODO FusionMAXMEANSUMPRODUCT No Norm. Min-max Z-score MAD Tanh Individual ModalitiesEERs Face Profile #1 Face Profile #2 Ear
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