U of HCOSC 6397 – Lecture 10 #1 U of HCOSC 6397 Face Recognition in the Infrared Spectrum Prof....

U of HCOSC 6397 – Lecture 10

#1

U of HCOSC 6397

Face Recognition in the Infrared Spectrum

Prof. Ioannis Pavlidis


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Primary Applications

• Biometric Identification– Passwords/PINs.

– Tokens (like ID cards).

– You can be your own password.

• Surveillance– Off-the-shelf facial recognition

system that identifies humans as they pass through a camera’s field of view.


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Novel Applications

• Wearable Recognition Systems – Adapt to a specific user and be more

intimately and actively involved in the user's activities.

– Face recognition software can help you remember the name of the person you are looking at.

• Useful for Alzheimer's patients.

• Smart Systems – Key goal is to give machines perceptual abilities that allow them to

function naturally with people.

– Critical for a variety of human-machine interfaces.


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Why Infrared?

• Visible light has no effect on images taken in the thermal infrared spectrum.

• Even images taken in total darkness are clear in the thermal infrared.


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Why Infrared? (Contd..)

• Illumination Invariance– Major problem in visible domain.

• Uniqueness and Repeatability– Sense thermal patterns of blood vessels under the skin,

which transport warm blood throughout the body.

– Remain relatively unaffected by aging.

– Even identical twins have different thermograms.

• Immune from Forgery – Disguises can be easily detected.


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Previous Work• Lot of research was done in the visible band but little attention was given in the

infrared spectrum.

• Recent reduction in the cost of infrared cameras and availability of large data sets encouraged active research in infrared face recognition.

• Low-Level Models– Directly analyze the image pixels and impose probabilities on the features.– Examples are PCA, ICA, and FDA.– Not good in challenging conditions.

• High-Level Models– Synthesize images from 3D templates of known objects and impose probabilities on

transformations.– Template matching approaches.– Computationally expensive.

• Our Proposal– Intermediate model which takes advantage of both Low-Level and High-Level models.


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Principal Component Analysis

• A D = H x W pixel image of a face, represented as a vector occupies a single point in D2-dimensional image space.

• Images of faces being similar in overall configuration, will not be randomly distributed in this huge image space.

• Therefore, they can be described by a low dimensional subspace.

• Main idea of PCA (cutler96):– To find vectors that best account for

variation of face images in entire image space.

– These vectors are called eigen vectors.

– Construct a face space and project the images into this face space (eigenfaces).


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Eigenfaces Approach - Training• Training set of images represented by

1,2,3,…,M

• The average training set is defined by

Ψ = (1/M) ∑Mi=1 i

• Each face differs from the average by vector Φi = Γi – Ψ

• A covariance matrix is constructed as:

C = AAT, where A=[Φ1,…,ΦM]

• Finding eigenvectors of N2 x N2 matrix is intractable. Hence, find only M meaningful eigenvectors. M is typically the size of the database.


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Eigenfaces Approach - Training

• Consider eigenvectors vi of ATA such that

ATAvi = μivi

• Pre-multiplying by A, AAT(Avi) = μi(Avi)

• The eigenfaces are

ui = Avi

• A face image can be projected into this face space by

Ωk = UT(Γk – Ψ); k=1,…,M


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Eigenfaces Approach - Testing

• The test image, Γ, is projected into the face space to obtain a vector, Ω:

Ω = UT(Γ – Ψ)

• The distance of Ω to each face class is defined by

Єk2 = ||Ω-Ωk||2; k = 1,…,M

• A distance threshold,Өc, is half the largest distance between any two face classes:

Өc = ½ maxj,k {||Ωj-Ωk||}; j,k = 1,…,M


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Eigenfaces Approach - Testing

• Find the distance, Є , between the original image, Γ, and its reconstructed image from the eigenface space, Γf,

Є2 = || Γ – Γf ||2 , where Γf = U * Ω + Ψ

• Recognition process:– IF Є≥Өc

then input image is not a face image; – IF Є<Өc AND Єk≥Өc for all k

then input image contains an unknown face; – IF Є<Өc AND Єk*=mink{ Єk} < Өc

then input image contains the face of individual k*


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Limitations of Eigenfaces Approach

• Variations in lighting conditions– Different lighting conditions for enrolment and query.

– Bright light causing image saturation.

• Differences in pose – Head orientation– 2D feature distances appear to distort.

• Expression – Change in feature location and shape.


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IR Face Recognition – Training Phase


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IR Face Recognition – Test Phase


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Segmentation

• Noise in the background may effect the performance of a face recognition system.

• Remove the background.

• Use thermal information on face to compute the features.

• Adaptive Fuzzy Segmentation (kakadiaris02)– Fuzzy affinity is assigned to spels w.r.t. target object spel.

– Affinity is computed as weighted sum of the temperature and the temperature gradient in the neighborhood of the target spel.

– Minimal user interaction because of dynamically assigned weights.


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Segmentation (Contd..)

• Fuzzy affinity is calculated by:

– Spatial Adjacency:


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Segmentation (Contd..)

– Temperature homogeneity & gradient:

– Weights:

- Temperature of seed c - Temperature of seed d

- Mean Temperature - Standard deviation of temperature


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Problem with Single Seed

• Temperatures on face are different at different regions.

• If a single seed is chosen in a particular region, then the connectivity stretches only along this region and the segmentation goes wrong.


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Multiple Seeds• Solution to this problem is to choose

multiple seeds in different regions on face and merge the resulting segmented parts .

• Choose a seed pixel on face wherever there is sharp change in gradient.

• Works well even when the subject is wearing glasses.

• Robust to variation of poses.


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Choosing Multiple Seeds


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Assumptions

• Merge all resultant segmented regions to form final image.

ASSUMPTIONS

• The center of the image contains the pixel from facial region.

• The temperatures at all pixels are mapped between 0 and 255.– If this mapped temperature at a pixel is between 175 - 200, it is classified

to be in blue region.

– If this mapped temperature at a pixel is between 200 - 225, it is classified to be in pink region.

– If this mapped temperature at a pixel is between 225 - 255, it is classified to be in cyan region.


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Feature Extraction

• The Gabor filter bank is given by:

• The segmented facial image is divided into its spectral components using

Gabor filters.• The resultant Gabor filtered images are modeled using Bessel models.


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Gabor Filter Bank• Example Gabor filter bank with 3 scale values and 4

orientation values:


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Spectral Components


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Bessel Parameters

• The filtered images are modeled using Bessel parameters:

SK – Sample Kurtosis

SV – Sample Variance

• Each segmented image in training set is convolved with the filters in

Gabor filter bank to obtain Gabor filtered images.


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Sample Variance and Kurtosis

• Sample Variance is the measure of the “spread” of the distribution.

• Sample Kurtosis is the measure of the “peakedness” or “flatness”.

Sample Kurtosis,


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Bessel Model

• Using the bessel parameters p and c, the filtered image I(j)(x,y) is modeled as:

(p) is gamma function Iv(z) is modified bessel function of first kind given by:


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Bessel Model


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Performance of Bessel K Forms

• Kullback-Leiber divergence:

KL div=0.0013 KL div=0.0027 KL div=0.0055 KL div=0.0058

– observed marginal density

– Estimated Bessel Form


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Comparing IR Images

• Images modeled into Bessel parameters can be compared by:

• L2-metric between two Bessel forms f(x;p1,c1) and f(x;p2,c2) in D:


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Hypothesis Pruning

• Applying a high-level classifier on entire database is computationally very expensive.

• Pruning of hypotheses can be achieved by using Bessel parameters (anuj01).

• Helps in short listing best matches.

• Bessel parameters for images in database can be computed offline which helps in saving a lot of computation time.


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Hypothesis Pruning (Contd..)

• Define a probability mass function on the database A:

(p(j)obs,c(j)

obs) – observed Bessel parameters for test image I(j)

(p(j),s,c(j)

,s) – estimated Bessel parameters which can be computed offline

• Images in database A with P1(|I) greater than a specific threshold value are short listed as best matches.

(D=0.3 for Equinox dataset)


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Hypothesis Pruning (Contd..) • Shortlist the subjects of A with P1(/I) greater than a specific threshold:


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Pruning Algorithm


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Classification• Bayesian target recognition (anuj00) searches for the target

hypothesis with largest posterior probability given by:

– Likelihood:

– Apriori is same for all images in database (for database of n images, it is 1/n for each image).

: Variance of test image

d : dimension of image (2 in this case)


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Experiments• Equinox Database:

www.equinoxsensors.com

• Image frame sequences were acquired at 10 frames/sec while the subject was reciting the vowels ‘a’,’e’,’i’,’o’,’u’.


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Results – ROC Curves

Correct Positive : Test image is in the database and is correctly recognized.

False Positive : Test image is not in the database, but is recognized to be an image of the database

Negatives : Test images that are not in the database.


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Results – Precision & Recall


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Conclusion

• We came up with a face recognition approach which is computationally inexpensive and at the same time good in challenging conditions.

• The features of all images in database can be computed offline and stored for future use. This saves lot of computation time.

• We improved the performance of classifier by removing background noise of pruned hypothesis using adaptive fuzzy connectedness based image segmentation.


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References

• [anuj01] A. Srivastava, X. W. Liu, B. Thomasson, and C. Hesher, "Spectral Probability Models for IR Images with Applications to IR Face Recognition," in Proceedings 2001 IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, Kauai, HI, Dec 14.

• [cutler96] R. Cutler, “Face recognition using infrared images and eigenfaces”, website, http://www.cs.umd.edu/rgc/face/face.htm, 1996.

• [anuj00] A. Srivastava, M. I. Miller, and U. Grenander, “Bayesian automated target recognition," Handbook of Image and Video Processing, Academic Press, pp. 869-881, 2000.

• [kakadiaris02] A. Pednekar, I.A. Kakadiaris, U. Kurkure. Adaptive fuzzy connectedness-based medical image segmentation. In Proc. of the Indian Conf. on Computer Vision, Graphics, and Image Processing (ICVGIP 2002), pp.457-462, Ahmedabad, India, December 16-18 2002.

U of HCOSC 6397 – Lecture 10 #1 U of HCOSC 6397 Face Recognition in the Infrared Spectrum Prof....

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