Face Recognition Antispoofing Methods Survey

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Corporate Systems & Technology
Rishabh shah
Review on Antispoofing techniques in Face recognition
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Biometric Antispoofing Methods : A survey in face
recognition
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Static includes 2D photo attacks while dynamic includes 3D mask and
Video attacks.

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› This paper works upon two parts 1) face detection 2) Motion and estimation tracking.
› Step 1:For face detection it uses a method called “QUANGLES”. It is faster than the other
methods.
› step 2: we resort to a simple algorithm that exploits motion to refine its effective
neighborhood.
› When a face is detected in the current frame f(t), we would like to know its most likely
counterpart in frame f(t−1).Then we select it’s center coordinates at a particular scale.
Verifying Liveness by Multiple Experts in Face
Biometrics
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› Suppose that we have a detected face x(t) at time t and we have δ(t) = (δx t , δy t )T ,
representing the motion vector between the center of two faces detected in the frames
f(t−1) and f(t), at our disposal. This helps us define the conditional probability density
p(X(t-1)|X(t)) for random vector :
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› Algorithm for the tracking of face and checking it liveness:
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5Stefan Voget © Continental AG

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› The last step is to collect evidence on liveness : It is done by working upon the 3D
properties of the face . But before that we need to crop the images to cover the face itself
and then start working upon it.
› We have two measures to take into consideration for to work upon 3D properties:
• Rasterflow: 3D motion of the face. This is checked by rasterflow method in which , we will
simply extract the means and standard deviations of |δt| within five equi-dimensioned
vertical stripe
› These five measurement pairs at the frame t are termed rasterflow_t, representing a five-
tuple (2x5 vector). They encode the spatiality and are expected to converge to a wedge-
pattern.
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› Eye flow : The eye regions can be sufficiently well located within the face patch because
they are likely to be at constant positions/scale (ex, ey, es) known from training of the
object detector.
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› By taking the mean of |δt| within these areas, and dividing it by the mean of |δt| in the
remaining light-gray area, we define our eyeflow(t) measurement.
› As the final step both raster flow and eye flow should be greater than zero or the face is
fake/spoofed.
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› The illumination and brightness plays a very important role in detection of the face. In order to
detect this kind of spoofs.
› So up until now DoG of the image in undertaken at different values of sigma to determine
different scales.
› DoG is taken so that the edges can be maintained but the noise from the image is removed and
it gets smoothened
› This model does not work under bad illumination because it used vanilla histogram equalization
method .
› In both the methods we are going to use sparse logistic regression with DoG as the frequency
descriptor.
Face liveness detection under bad illumination condition
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› Proposed method is called CLAHE(Contrast limited adaptive histogram equalization).
› Every tile within the image has it’s contrast enhanced w.r.t to the type of output histogram
expected like a uniform(flat) one , Rayleigh(Bell-shaped) or an exponential(Curve shaped)
one.
› It depends on three things which are : 1) No. of tiles 2)Contrast enhancement limit
3) alpha value.
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› This paper is based on the movement within the facial components. So the movement in
the eyes in the sequential images will be checked.
› Algorithm :
Liveness detection for embedded face recognition
system
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› Eye Detection : When the face image is considered as a 3D curve, the intensity of the eye
region is lower than the rest of face region. So to get eye region we perform two steps:
 First, we perform Gaussian filtering to the face image, so that the smoothened 3D curve is
obtained.
 In the curve, we extract all the local minimums using the method of the gradient descent .
› To reduce the invalid eye candidates, we used the eye classifier, which is trained by Viloa’s
AdaBoost training methods .
› Face Region Normalization :Input face can vary in size and orientation, we normalize
face region about a size and rotation by using center points of both eyes. we normalize this
face region to the size of 72x72.
› Then we apply SQI to the face region to decrease the effect of illumination. Self Quotient
Image (SQI).
› Î is the low frequency image of the original image, F is the Gaussian kernel
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› In the below given images the first row ones are original images under various lighting
condition while the later ones are it’s SQI.
› Eye Region Binarization : After Normalizing face region, eye regions are extracted as
10x20 size based on the center of eyes. Then eye regions are binarized in order to have
the pixel value of 0 and 1 by using a threshold.
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› Figure shows the example of binarized eye regions extracted from 5 sequential face
images. As shown in Fig. (a) Eye regions of fake face change very little, but eye regions
(b) of real face have a much larger variation in shape because of blink or movement of
pupil.
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› Liveness score calculation
› Steps:
› 1) Hamming distance(the Hamming distance is the number of pixels that do not have same
value) method is used to calculate liveness score of each eye region
› 2) We compare 5 left eyes each other using hamming distance and 5 right eyes in the
same way
› 3) The number of pixels which differs between two eye regions becomes liveness score.
› 4) After adding 10 liveness scores of left eyes and 10 liveness scores of right eyes, we
take an average of scores.
› 5) If the average liveness score is bigger than threshold, we recognize the input image as
live face and in the case of opposite it is discriminated to the photograph.
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› So this paper takes into consideration three different kinds of scenarios:
› 1) Non- rigid motion clues i.e. Eye blink
› 2) Face background consistency clue
› 3) Image banding effect – Quality changes in the image due introduction of fake face.
Non- rigid motion clues
› We adopt the batch image alignment method to separate the non-rigid motion from the
rigid ones
› Batch image alignment utilizes a series of rigid transformation to align several images to a
fixed image, with the residual being the non-rigid motion.
› Image in the next slide shows how this alignment procedure works :
Face Liveness Detection by Exploring Multiple scenic
clues
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Corporate Systems & Technology 17.07.2016
For genuine face sequence,
there are some frames that have
large sparse residual in eye
regions which corresponds to
eye-blinking or in mouth regions
which corresponds to mouth
movement.
There may be some residual in
fake face video, such as the 1st
and 6th frame in Fig. (b),
however they are uniformly
distributed noises in the whole
face region.

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› Facial Non-rigid motion features :
If a face sequences have a big Si like Fig. 1, the corresponding face is considered genuine;
otherwise fake.
› Face Background Consistency:
› It is based on the observation that if the target face is genuine, its motion should be totally
independent from that of the background; otherwise the motion relationship between face
and background cannot be totally independent because of the exhibition medium’s
constraint.
› We do not use optical flow estimation method.
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› GMM(Gaussian Mixture Model) is used since it is more efficient and it is robust to
illumination and noise.
› After some initialization, GMM outputs the foreground, background binary image
Bi , i = 1 · · · , n of a given frame I :
› This Bi is further used to check motion trend, which is the first step of checking the
consistency;
› It will be further used to calculate the CMD(Consistency Motion Descriptors).It is the first
dimension of face-background consistency feature.
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› Motion entropy : Second dimension of face-background consistency
› The motion of hand hold photo attack videos are global while the motion of genuine videos
are local. This inspires us to use the total motion as another measure. The motion entropy
me(i), i = 1, 2 · · ·,n is defined as:
› Pi = Foreground ratio
› The motion entropy of handed fake face is very large compared with genuine face, while
the motion entropy of fixed fake face is close to 0.
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› Image Banding : Since fake faces are usually exhibited by certain medium, inevitably
some quality degradation will be introduced during the imaging process.
› Face liveness detection by wavelet decomposition.
› Image noise can be estimated by a robust median estimator as follows:
› HH1 means the first order wavelet decomposition of the image
› We find that the median estimator values of fake face images are all near 0.74 while most
genuine faces are near 0 with only two exceptions.
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› Contrast and texture analysis of recaptured and captured images.
› Earlier, DoG (Difference of Gaussian) filter is used to obtain a special frequency band
which gives considerable information to discriminate between real and photo images.In this
approach it used for preprocessing.
› LBPV: It is a combination of spatial structure and the contrast.
› In LBPV global spatial information is not lost.
› In this approach, initially rotation variant LBPV histograms are obtained.
› Process of this approach :
Classification of Captured and recaptured Images to
detect photograph spoofing
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› Variance is computed for the P sampling points around a circle of radius R, after the
calculation of LBP at the same radius R.
› &
› The final equation to calculate LBPV:
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› Global matching is implemented using exhaustive search scheme to find the minimal
distance in all candidate orientations, which is a simple method.
› The LBPV histogram is reorganized and represented by a rotation variant histogram Hrv,
and a rotation invariant histogram Hri. Then for two texture images, the matching distance
is calculated.
› Finally, each test image is classified in the nearest class by comparing the two quadratic
mean values, which are obtained for client and impostor sets.
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› This paper functions for enhanced videos for spoofing detection with the help of two
techniques.
› Spoofing based on text features extracted using LBP.
› Spoofing based on motion features extracted using MOOF.
Eulerian Motion Magnification
› First, each frame is decomposed into spatial Laplacian bands. Next, an ideal temporal
bandpass filter is applied to each Laplacian band to isolate the desired temporal motion in
each band
› The isolated band passed signal is then multiplied by an amplification factor α and added
to the original signal, as shown in eqn.
Computationally Efficient Face Spoofing Detection with
Motion Magnification
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› LBP method:
› HOOF method: Histogram of oriented flows
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› This paper focuses on the usage of many low level feature descriptors for detection of the
spoofed image.
› CF: color frequency, it used to store the color information regarding the image.
› HSC: Histogram of Shearlet coefficients, these are used to estimate edge response.
› HOG: To capture the edge’s gradient structure.
› GLCM: Gray level co-occurrence matrix. It is used to show texture or the gray level
pattern.
› This four help in extracting 12 feature descriptors.
Face spoofing Detection through partial least squares
and low level descriptors
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› So the feature extraction technique used over here are EUC-LBP (Extended Uniform
Circular LBP) and SIFT.
Recognizing surgically altered face using multiobjective
evolutionary algorithm
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› We have to use EUC-LBP and SIFT on all the 40 granules:
› SIFT works better and for some the EUC-LBP works better so they propose to use
evolutionary algorithm like Genetic Algorithm to decide the fitness function.
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Face Recognition Antispoofing Methods Survey

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