human recognition based on facial profile and ears
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HUMAN RECOGNITION BASED ON FACIAL PROFILE
AND EARSFırat Onur Alsaran, Neşe Alyüz, Melike Esma İlter,
Mürsel Taşgın, Ahmet Burak Yoldemir
CmpE 58Z Term Project
EAR RECOGNITION USING LOCAL BINARY PATTERNS
Ahmet Burak Yoldemir
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
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
Samples from the database
Person 1:
Person 2:
High illumination variance!
First attempts
Filter bank approaches are applied firstAngular radial
transformGaborfilters
Leung-Malikfilters
Schmidfilters
First attempts
Filter bank approaches are applied firstAngular radial
transformGaborfilters
Leung-Malikfilters
Schmidfilters
First attempts
Filter bank approaches are applied firstAngular radial
transformGaborfilters
Leung-Malikfilters
Schmidfilters
First attempts
Filter bank approaches are applied firstAngular radial
transformGaborfilters
Leung-Malikfilters
Schmidfilters
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
Local binary patterns - Advantages Tolerance against illumination
changes Computational simplicity A compact description of the image
Local binary patterns - Example
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
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
Recognition step
Euclidean distance between these feature vectors is used as the (dis)similarity measure
A similarity matrix is formed using these distances
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
Results – Without Gaussian filtering
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FMR
GA
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Receiver Operating Characteristic Curve (not filtered)
OriginalMulti-presentation (mean)Multi-presentation (max)
Method EER (%)
Original 32.19MP (max) 14.73MP(mean
) 1.77
Results – With Gaussian filtering
Method EER (%)
Original 13.18MP (max) 5.43MP(mean
) 1.14
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FMR
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Receiver Operating Characteristic Curve (filtered)
OriginalMulti-presentation (mean)Multi-presentation (max)
FACE PROFILE MATCHING
Mürsel Taşgın
Facial Profile recognition
Motivation Facial profile images can be collected
from side cameras Computation complexity is lower Complementary solution for face
recognition
Profile Database
448 profile photos from Multi-PIE database
112 subjects, each having 4 photos
Facial profiles are extracted manually in the first place
Facial Profile Registration
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1 2 Rotate 90º CW 3Extract profile Edge detection 4
56 Scale and move to top (nose at the center)
Chin &
nose detection using gradient of image
Nose at the center and touching top
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Histogram representation (image to function) gradient
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
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lips Image-filling detects lips, so use gradient to find chin
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
MULTI-BIOMETRIC FUSION OF FACIAL PROFILE AND
EAR
Neşe Alyüz
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
Score Normalization Techniques
Min-max normalization Z-Score normalization Median Absolute Deviation (MAD)
normalization Tanh normalization
Min-max Normalization Best suited for the case where bounds
are known Shift scores into range [0 1] Given a set of matching scores: {sk} Normalized scores:
Original distribution is kept, only scaling
When bounds are estimated, not robust to outliers
Z-score Normalization Performs well if prior knowledge is
available Mean and standard deviation are used Given a set of matching scores: {sk} Normalized scores:
Original distribution is not retainedDoes not guarantee a common numerical rangeWhen mean and std are estimated, very
sensitive to outliers
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(|sk - median|)
Median and MAD have low efficiencies When score distribution is not Gaussian, poor estimatesInput distribution is not retainedNormalized scores are not in a common range
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
Score Fusion Techniques
MAX rule MEAN rule SUM rule PRODUCT rule
Evaluated on scores that are normalized with different approaches
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
Experimental Results - EERs
Fusion MAX MEAN SUM PRODUCTNo Norm. 8.58 8.27 8.27 14.01Min-max 14.87 8.64 8.64 8.32Z-score 8.15 7.89 7.89 20.42MAD 7.88 7.86 7.86 18.58Tanh 7.84 7.61 7.61 7.57
Individual Modalities EERsFace 12.09Fingerprint 21.76
Experimental Results - TODO
Fusion MAX MEAN SUM PRODUCTNo Norm.Min-max Z-scoreMADTanh
Individual Modalities EERsFace Profile #1Face Profile #2Ear
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