Face Spoofing Detection using Hybrid Kernel Approach with CNN, SVM Classifiers

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Vol. 28, No. 16, (2019), pp. 946-953

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Face Spoofing Detection using Hybrid Kernel Approach with CNN, SVM Classifiers

1Kasetty Lakshminarasimha, ²Dr. V. Ponniyin Selvan

1Research Scholar, ²Professor

1,2Department Electronics and Communication Engineering

1,2 Mahendra College of Engineering, Salem.

1 [email protected], ²[email protected]

Abstract -- Face antispoofing methods has been built up quite a while since the face affirmation frameworks were satisfactorily related. The standard methods in this subject simply utilize the whole area of person face. Regardless, uncommon facial parts continually have different structures and the full-face model possibly debilitate the error of the particular parts. Thusly, setting up the particular model for every facial part can improve the execution of against spoofing. Here, we propose another procedure of face against spoofing utilizing half and half CNN for facial parts. We separate the face into a couple of areas and dependent on different parts, applying the CNN representation for it, which will set up the crossover DCT-CNN. Moreover, we connect on the cross breed model to prepare a SVM classifier. Utilizing SVM, The face picture is portioned into number of various squares and LBP Features are taken, at that point SVM is utilized for deciding if the information picture relates to live or counterfeit face. We tried the proficiency of our procedure on open available databases, picture, video, mask attacks and the investigations exhibit our proposed methodology can secure acceptable results with respect to the top class methodologies.

Keywords: Face-spoofing attack, Local Binary Pattern, CASIA dataset, SVM, CNN

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Introduction

In ongoing mechanisms in specific procedures a proposed to find the live object faces with non-live object faces from its spoofing attacks. A down to earth face acknowledgment framework requests high acknowledgment execution, yet additionally the ability to discover the liveness of existing objects.

The extricated highlights are coordinated against the highlights in the database and afterward, the classification of the client is distinguished. At long last, liveness of the client is to be tried in order to counteract the spoof assault. There are numerous methodologies like single picture based methodology manage the particular attributes from the perfect pictures taken from live faces and masks, though various picture based methodology is as of 3-D facial data and additionally from eye-flicker data. The areas and disseminations of pictures fluctuate from object to object and authentic picture of one object cover the phony picture of another object. In this manner, it is hard to discover non-live object with a solitary enemy of spoofing system. To take care of this issue, we enhance improved feature descriptors for Object Face Liveness Detection utilizing DCT-CNN Classifier for Biometric Authentication Systems.

In object face liveness discovery techniques, we create against spoofing process used for each enrolled objects in database. Be that as it may, many enlisted objects in database comprising of both veritable examples and conceivable phony examples. To prepare object face liveness models the proposed technique depends on the suspicion that the connection between veritable examples and fake examples of two subjects are both brought about by the difference in character, and along these lines be comparative commonly. We initially catching the pictures and choosing Region of Interest (ROI) from caught test picture, and extricating different highlights from these chose pictures, by applying these separated highlights to the fake pictures from live pictures during classifier.

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Then again, face pictures caught from printed photographs seem to be like the pictures where caught legitimately from the sensor as appeared in the beneath Figure.1. The principal push shows genuine face pictures where the subsequent line shows counterfeit face picture from CASIA database. There is no unmistakable distinction between genuine face images and faker face images. Be that as it may, there's a distinction between the two lines when we take a gander at the pictures from surfaces perspective.

To adapt to this issue an novel descriptor for facial picture spoofing which depends on LBP; the highlights are removed from the neighborhood facial picture regions so as to handle the issue of recognizing counterfeit facial biometric information. In this work we use SVM as a ML model without a doubt/counterfeit face classification. We will likely recognize the spoofed face picture from surface investigation perspective.

Biometric applications are expanding step by step and it is more secure than some other username, secret key and so on. Unique finger impression, face, and iris are the biometric attributes most much of the time utilized in present authentication systems. Biometrics may utilize physical or conduct qualities for distinguishing proof purposes. Face is the significant biometric as its common, straightforwardness to utilize and non-rudeness. Face is increasingly well known as it doesn't require any extra equipment and practically all cell phones are furnished with forward looking camera. Nonetheless, the issue of spoofing attacks can challenge face biometric systems in useful application. Spoofing attacks can without much of a stretch dispatch by photograph attacks, video replays and 3D masks of the face. Ongoing headways, for example, plastic medical procedure, 3D face veil and effectively accessibility of pictures and recordings in the informal community help the aggressors to spoof the framework. Despite the fact that few face spoofing location strategies have been proposed, the issue is as yet unsolved because of touchy requirements and restrictions.

Related Work

Unfortunately, look into solutions to this sort of assault has not kept-up-regardless of whether such dangers have been known for almost 10 years. There appears to exist no unity on best practices, procedures or conventions for creating and test spoofing identifiers for face acknowledgment [3].

Anti spoofing for 2D face acknowledgment system can be roughly characterized into 3 classes as for the hints utilized for assault discovery: movement, texture examination and liveness location [4].

Li et al. [9], utilized a Fourier spectra to think about the printed copy of customer face and genuine gets to. Li et al's. Technique functions admirably for down tested of the produce photograph assault character, yet it comes up short for better pictures now and then.

Liveness location attempts to catch indications of life from client pictures by investigating unconstrained developments that can't be recognized in photographs, for example, eye squinting. The creators in [2]

brought continuous liveness recognition explicitly against photograph spoofing utilizing unconstrained eye-squints, which should happen once every 2-4 seconds in people.

Face acknowledgment systems are known to react feebly to attacks for quite a while [9, 10] and are effectively spoofed utilizing a straightforward photo of the enlisted individual face. Antispoofing for 2-D face acknowledgment system can be coarsely ordered into 3 classifications as for the pieces of information utilized for assault identification: motion, texture investigation and liveness discovery [11].

Li et al. [14], utilized a Fourier spectra to analyze the printed versions of customer face and genuine gets to. This technique functions admirably for down-tested of the print-photograph assault character, yet it comes up short for greater pictures some of the time.

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Another class of anti-spoofing techniques center around discovery of a live-face explicit motion on the scene, for example, eye flickering, mouth developments or head developments. Instances of strategies utilizing eye-squinting discovery are proposed in [19, 20].

LBP [17,18] has developed as one of the most conspicuous texture highlights and a considerable number of new variations keep on being proposed. LBP's qualities incorporate maintaining a strategic distance from the tedious discrete jargon pre-preparing stage in the BoW(Bag of Words)framework, its general computational effortlessness, its monotonic brightening invariance, its adaptability, and simplicity of execution.

Face Anti-Spoofing Approach

In this segment, we clarify our methodology of anti-spoofing used to separate between live faces and phony ones. The square outline of “our anti-spoofing approach can be found in Fig. 2. To begin with, we identify the face utilizing Viola-Jones algorithm [17] and we at that point apply the Active Shape Models with Stasm [18] to find tourist spots. These tourist spots help us to alter and edit the faces. After that we isolated the face image into 16x16 overlapping regions, and we applied LBP administrator on every region. The neighborhood 243-receptacle histograms from every region are processed and gathered into a solitary 2187-canister histogram and from that point forward, we applied fisher score for lessening the quantity of histogram containers”. At long last, we utilized a non-straight SVM classifier with outspread premise work part for deciding if the info image compares to a live face or not. We depict underneath each progression in detail.

Fig: Proposed implementation procedure Spoofing Detection using LBP

Here, we give details our system of anti-spoofing used to isolate between live faces and fake ones. The square graph of our antispoofing approach is as showed up in Figure. 2. First we isolated the face picture into 3x3 covering areas in the image, and we applied LBP administrator on every region. At long last, we utilized a non-direct classifier of SVM by spiral premise work kernel for deciding if the information image compares to a live face or not. We portray below each progression in detail.

Feature Detection using LBP:

The LBP is a picture overseer which changes a picture into a bunch or picture with more detail. “The fundamental LBP introduced by Ojala et al.[12], relied upon the supposition that surface has locally two correlative perspectives, an example and its quality. The first LBP works in a 3x3 pixel square of picture.

The pixels in this square are edge by its inside pixel, increased by forces of two and afterward added to get a mark for the middle pixel”. As the image regions comprises of 8 pixels, a sum of 2⁸=256 various labels can be acquired relying upon the relative dim estimations of the middle and its image regions as appeared in Figure.4.

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The estimation of the LBP code of a pixel (xc, yc) is given by:

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Where gc relates the dark estimation of middle pixel(xc, yc), gp alludes to dim estimations of P similarly separated pixel on a circle of radius-R , and s characterizes a threshold function as follows:

(2) Features Histogram Reducing using Fisher Score(FS)

FS [22] is mainly recognized technique for highlight determination. The thought in FS is to choose each element autonomously as indicated by its scores under the fisher standard. We utilized FS in our way to deal with lessen the receptacle histogram and keeps to use the useful histograms.

DCT-CNN

Ongoing work in face liveness identification has been founded on the utilization of profound CNN models as give preferred liveness discovery exactness over the recently referenced methodologies. The work proposed in concentrated on preparing profound CNNs for liveness discovery by utilizing information randomization on little smaller than expected clumps. The examination proposed used a mix of dissemination of the info face pursued by just three-layer CNN engineering.

In our technique, “we proposed an answer where we initially applied nonlinear dissemination dependent on an added substance administrator parting plan and an effective block-solver called the tridiagonal framework algorithm to the caught image so as to improve the edges and protect the limit areas of the image. These diffused info images were then nourished to the CNN design to separate the intricate and profound highlights and to at long last characterize the image as genuine or fake”. We utilized three distinctive CNN designs and played out a relative assessment of their exhibition, along these lines picking up knowledge into why a specific engineering is more qualified for face liveness location.

Fig: CNN architecture

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SVM Classification

A SVM perform order by discovery the hyperplane that expands the edge amid 2 classes. The vectors (cases) that characterize the hyperplane are known as the help vectors. In our analyses, “when the improved histograms are figured and diminished, we utilize a nonlinear SVM classifier with outspread premise work part for deciding if the information picture compares to a live face or not. The SVM classifier is first prepared utilizing a lot of positive (genuine faces) and negative (fake faces) tests from the dataset”.

Fig: Steps in face spoofing Dataset Collection

CASIA face antispoofing database: “The CASIA database contains 50 veritable subjects, and fake faces are produced using the great records of the certified faces. Three imaging characteristics are considered, to be specific the low quality, typical quality and high caliber. Three fake face attacks are executed, which incorporate distorted photograph assault, cut photograph assault and video assault. Hence each subject contains 12 recordings (3 real and 9 fake), and the last database contains 600 video cuts (240 for train and 360 for test)”. Test convention is given, which comprises of 7 situations for an intensive assessment from every single imaginable perspective.

In our tests, we utilized discriminate cosine transform (DCT) to find all segments of the face images and Stasm for limiting the eyes. “The headings of the eyes are used to go and to alter the face. Each and every cut face is resized to a dependable size 64x64. By then, we confined the face into nine blocks to apply the covering calculation (see Fig. 2). On each square we apply LBPU2 (16,2) which gives a histogram of 243 canister. All histograms are then associated into a single histogram of 2187 compartment. Finally, we use fisher score for diminishing histograms. To classify the faces, we use SVM classifier with a nonlinear RBF kernel. The parameters of the SVM classifier were resolved utilizing a grid search”.

Results Analysis

We led tests utilizing the CASIA dataset [2]. First the image is pre-prepared into dim scale. Viola-Jones is applied to recognize face and it is standardized to estimate of 160 x 160 pixels. The standardized face is

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divided into 100 blocks; each block is size of 16 x 16 pixels. Uniform LBP descriptor is applied to each block, extricating 59 highlights which is rehashed for every residual block to get 5900 highlights. This procedure is rehashed for every one of the images of dataset including genuine and fake mages.

Table I: Results of images whose Block size of 16 x 16

The CASIA antispoofing face database including 14 classes of original and spoof pictures. “The main picture of dataset is resized into 160x160 pixels. During preparing of SVM classifier 10 pictures were considered in each class and a while later attempted. Later number of pictures was extended from to 20 and 100 in each class for preparing. The testing set including both authentic and spoof total of 1150 advancement 1949 pictures independently”. For all classes, each picture is isolated into 100 blocks and each square is of size 16 x 16 pixels. For all pictures full scale number of LBP features removed is 5900.

It has been seen from Table I that as the quantity of preparing pictures in preparing set grows the level of affirmation rate furthermore increases (both without a genuine and spoof pictures).

Fig.: Accuracy of Liveness detection

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Conclusion and Future work

This paper enhanced work is philosophy for antispoofing discovery subject to LBP and FS that segregate live face from fake ones. Our philosophy took a stab at CASIA face antispoofing databases which contains a couple of authentic and fake faces showed promising results stood out from various past works.

As future work we will endeavor to test our technique on various databases and find another system working commendably on brilliant pictures or chronicles.

References

1. F. L. Podio, “Biometrics technologies for highly secure personal authentication,” National Institute of Standards and Technology, http://whitepapers. zdnet. com/search. aspx, 2001.

2. G. Pan, Z. Wu, and L. Sun, “Liveness detection for face recognition,” Recent advances in face recognition, pp. 109–124, 2008.

3. W. R. Schwartz, A. Rocha, and H. Pedrini, “Face spoofing detection through partial least squares and low-level descriptors,” in Biometrics (IJCB), 2011 International Joint Conference on. IEEE, 2011, pp. 1–8.

4. A. Anjos and S. Marcel, “Counter-measures to photo attacks in face recognition: a public database and a baseline,” in Biometrics (IJCB), 2011 International Joint Conference on. IEEE, 2011, pp. 1–7.

5. O. Kahm and N. Damer, “2d face liveness detection: An overview,” in Biometrics Special Interest Group (BIOSIG), 2012 BIOSIG-Proceedings of the International Conference of the.

IEEE, 2012, pp. 1–12.

6. K. Kollreider, H. Fronthaler, and J. Bigun, “Evaluating liveness by face images and the structure tensor,” in Automatic Identification Advanced Technologies, 2005. Fourth IEEE Workshop on.

IEEE, 2005, pp. 75–80.

7. K. Klaus, H. Fronthaler, and J. Bigun, “Verifying liveness by multiple experts in face biometrics,” in Computer Vision and Pattern Recognition Workshops, 2008. CVPRW’08. IEEE Computer Society Conference on. Ieee, 2008, pp. 1–6.

8. W. Bao, H. Li, N. Li, and W. Jiang, “A liveness detection method for face recognition based on optical flow field,” in Image Analysis and Signal Processing, 2009. IASP 2009. International Conference on. IEEE, 2009, pp. 233–236.

9. J. Li, Y. Wang, T. Tan, and A. K. Jain, “Live face detection based on the analysis of fourier spectra,” in Defense and Security. International Society for Optics and Photonics, 2004, pp. 296–

303.

10. N. M. Duc and B. Q. Minh, "Your face is not your password", in Proceedings of Black Hat Conference, 2009.

11. M. M. Chakka et al., “Competition on counter measures to 2-d facial attacks,” in Proceedings of International Joint Conference on Biometrics, 2011.

12. T. Ojala, M. Pietikainen, and T. Maenpaa, “Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns” in proceedings of Pattern Analysis and Machine Intelligence, IEEE, vol. 24, no. 7, pp. 971–987, 2002.

13. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features”, in Proceedings of Computer Vision and Pattern Recognition, Vol. 1, pp. 1-9. 2001.

14. J. Li, Y. Wang, T. Tan, and A. K. Jain, “Live face detection based on the analysis of Fourier spectra”, in Proceedings of Defense and Security. International Society for Optics and Photonics, pp. 296–303, 2004.

15. Tan X, Li Y, Liu J, et al. ,“Face liveness detection from a single image with sparse low rank bilinear discriminative model”, in Proceedings of European Conference on Computer Vision(ECCV), Springer-Verlag, pp. 504-517, 2010.

16. Parveen S, Ahmad S, Abbas N, et al. “Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP)”, Computers: Open Access Journal, Vol. 5, No.2, pp. 1-15, 2016.

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17. W. Bao et al. “A liveness detection method for face recognition based on optical flow field”, in Proceedings of International Conference on Image Analysis and Signal Processing, IEEE, 2009.

18. Pietikainen, M, Hadid, A., Zhao, G., Ahonen, T,” Computer vision using local binary patterns”, Springer, London, UK, 2011.

19. G. Pan, Z. Wu, and L. Sun, “Liveness detection for face recognition: Recent Advances in Face Recognition”, Vienna, Austria, 2008.

20. H. K. Jee, S. U.Jung, and J. H. Yoo, “Liveness detection for embedded face recognition system”, International journal of computer and information Engineering, Vol. 2, No.6, 2008.

21. K. Kollreider, H. Fronthaler, and J. Bigun, “Non-intrusive liveness detection by face images”, in Journal of Image and Vision Computing, ELSEVIER, vol. 27, no. 3, 2009.

Authors Information

Kasetty Lakshminarasimha is a research scholar in Information and Communication Engineering department in ANNA University. He has a total teaching experience of 8 years. Area of research is Image processing. Completed UG B. Tech (Electronics & Communication Engineering) in the year 2007 from JNT University, Hyderabad. Completed PG M. Tech (VLSI Design) in the year 2012from JNT UNIVERSITY, Hyderabad. Affiliated to ANNA University Registered PhD in the year 2018.

Dr. V. Ponniyin Selvan is a research supervisor in Information and Communication Engineering department in ANNA University. He is currently working as professor in faculty of Electronics and Communication Engineering at Mahendra College of engineering, Salem, Tamilnadu. He has a total teaching experience of 17 years. Area of research is Networks, Image processing. Completed M.E in the APPLIED ELECTONICS in the year 2005 from ANNA University and Completed PhD in the networks in the year 2014 from ANNA University.