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1 Reviewed Paper Volume 3 Issue 6 February 2016 International Journal of Informative & Futuristic Research A Novel Method For Face Spoof Detection With Image Quality Distortion Parameters Paper ID IJIFR/ V3/ E6/ 056 Page No Research Area Keywords Digital Image Processing Face Detection, Spoof Detection, IDA, IQP, Neural Classifier, Cross database 1 st Chithra S Ravi 2 nd Deepa Thomas M.Tech. Student Department of Computer Science Engineering Musaliar College Of Engineering & Technology Pathanamthitta, Kerala-India Assistant Professor Department of Computer Science Engineering Musaliar College Of Engineering & Technology Pathanamthitta, Kerala-India Abstract Automatic face detection is used in many applications mainly for mobile payment. This popularity distress some face spoof attacks, which is a photo or video of an endorsed person face used to access the services. This work proposes an effective face spoof detection algorithm based on an Image Distortion Analysis (IDA) and Image Quality Parameters (IQP). Four features are used to form the IDA feature vector which is specular reflection, blurriness, chromatic moment, and color diversity. A Neural classifier can be used in an IDA and IQP, that could be trained each features of data for detect different face spoof attack. It helps as to differentiate between genuine and spoof faces and also detect the type of attack. This work can be used a face spoof data base, which is MSU Mobile Face Spoofing Database (MSU MFSD). Proposed solution overcomes the problems of an ensemble classifier, in the detection of spoof images. It separate genuine and spoof faces successfully on the basis of full reference method and clearly define the type of attack. 1. INTRODUCTION Automatic face recognition increases the attention in different access control applications. Face recognition does not need any further sensors, because all smartphone have a front camera. It is easy to acquire a person s face image or video than other biometric traits such as fingerprint, palm print, and iris. Face spoof attack mainly occur Available online through Published On: February 29,

2 as a printed photo or video frame form. An ensemble classifier face detection systems are not well designed to distinguish spoof faces from genuine live faces, which is not more than 70% of spoof faces are successfully matched to the gallery mates at rank-1. Proposed solution is IDA and IQP which is used for detecting spoof faces successfully with the help of a neural classifier. In spoof face attacks, mainly used to check the fragility of face recognition successfully Ivana [1]. 2. PRIOR WORK Face spoof detection was firstly reported in 2004, which is focus on the FP7 EU funded project and TABULA RASA are some spoofing attacks in the state of the art Commercial Off-The-Shelf(COTS). According to various types of cues used in face spoof detection, different methods can be categorized into four groups (i ) Motion based methods(ii) Texture based methods(iii) Method based on image quality analysis(iv) Method based on other cues. a b c Figure 1: (a) A genuine face image (b) Printed photo (c) Video frame. 2.1 Motion Based Methods These motion based methods, designed to count printed photo attacks, confine a very important cue for vitality, the intuitive motion of organs and muscles in a live face, such as eye blink, mouth movement and head rotation. The frequency of facial motion is limited by the human physiological rhythm, ranges from 0.2 to 0.5 Hz. Finally, it takes a comparatively long time usually greater than three seconds to acquire stable vitality features for face spoof detection. 2.2 Texture Based Methods To oppose the printed photo and replayed video attacks, texture based methods were suggested to remove image artifacts in spoof face images. In texture features are capable of distinguish artifacts in spoof faces from the genuine live faces. Texture based methods S. Marcel [3] have a success on the Idiap and CASIA databases. Generalization ability of texture based methods is to be poor. The half total error rate increased dramatically under the cross-database, where the training and testing sets came from dissimilar face spoof databases. 2.3 Image Quality Analysis Based Method There is no face specific information has been measured in designing informative features for face spoof detection. On the divergent, four features are designed exclusively for face feature representation in image quality analysis method, and the effectiveness of these features for spoof face detection. Idiap and CASIA databases, are two important public-domain databases. The training and testing of each modality were evaluated under intra-database. The proposed approach focus is to improve the 2114

3 generalization ability under cross-database which has often explored in the biometric community. 2.4 Methods Based On Other Cues Face spoof counter measures using cues which is derived from sources than 2D intensity image, such as 3D depth, IR image Zhang [2], spoofing context, and voice have been proposed. This work imposes additional necessities on the face recognition system. These four types of methods can also be collective to utilize multiple cues for face spoof detection. Thus motion magnification restricted by human physiological rhythm, cannot reach the reported performance F. Crete [6] without accumulate a large number of video frames (>200 frames), making these methods not suitable for real-time response. As a result cues derived from some image, spoofing image, and voice G. Chetty [4]. 3. FEATURES DERIVED FRON IDA A genuine face or a spoof face is presenting to a camera in the same imaging environment, the difference between genuine and spoof face images is due to their shape and characteristics of the facial surface in front of the camera. This work based on the Dichromatic Reflection Model, light reflectance I of an object at a specific location x can be decaying into the diffuse reflection (Id) and specular reflection (Is). In an IQP, contain three parameters (1) Mean Square Error (MSE) (2) Peak Signal to Noise Ratio (PSNR) and (3) Signal to Noise Ratio. This work includes MSE for checking high values in their gray scale range, in between This work use Gaussian Filter (GF) for image smoothing, which is a high frequency filter and it creating a filtered image from input image. Both of as compared and extract the values of MSE, PSNR, and SNR. = ( )/ = 2552 =. ^ / 3.1Printed Photo Attack In printed photo attack, light reflectance of an object in a specific location is first distorted to the printed ink intensity on the paper, and goes to the final image intensity from the paper surface through the refraction. During this transformation blurring the original face image and distorting color intensity are decided by the printer frequency and chromatic fidelity. For high resolution color printer, the distortion of blurring the original face image can be avoided, but not for distorting color intensity. Thus image distortion in printed photo attack can nearly by a contrast humiliating transformation. 3.2 Replay Video Attack Light reflectance of an object in a specific location is transmitting to the radiating intensity of pixels on the LCD screen. Frequency band width of the LCD panel is used for determine the original face image s blurriness. Distorting color intensity is related to 2115

4 the LCD color distortion and properties of intensity transformation. Based on the above analysis, the major distortions in a spoof face image (1) Specular reflection from the printed paper surface or LCD screen (2) Image blurriness (3) Image chromaticity and contrast distortion (4) Color diversity distortion. The video frame attack, identifying the pixel intensity of its background color. It may depend upon the light reflectance of that specific location. Color distortion calculated on different ways, on the basis of its signal to noise ratio and peak signal to noise ratio. Four different IDA features are extracted for each normalized face image. As the intensity of blurriness is high video attack occurs. Thus detect the type of attack successfully and get spoof image. The spoof face which is differentiate from its genuine face on another way, by the difference between the specular reflectance and diffuse reflectance. Figure 2: Face spoof detection using IDA and IQP with a neural classifier As indicated above figure, the diagram of the proposed spoof detection algorithm based on IDA and IQP with neural classifier. An input face image is aligned on two eyes locations which are normalized into pixels with an interpupillary distance (IPD) of 60 pixels. Experiments shows that face position and cropped face size are very important for spoof detection because they are extensively decrease the influence of facial and background variations that are inappropriate to spoof detection. Four different IDA features are extracted for each normalized face image which constitutes a 121-dimensional feature vector. This feature vector fed into a neural classifier, which act as multiple classifiers. Basically it is a single classifier, each trained on a different group of spoof training samples in the database. The classifier outputs are join to give the final binary decision which is genuine or spoof face and also give type of attack Specular Reflection Features To separate the specular reflection component Is from an input, which is used for an iterative method proposed ] R. T. Tan [5], which assumes that the illumination is i) from a particular source, ii) of unique color, and iii) not over-flooded Thus the scheming specular reflection component image Is, it signify the intensity distribution with three dimensional features: i) specular pixel percentage r, ii) mean intensity of specular pixels μ, and iii) variance of specular pixel intensities σ. 2116

5 3.1.2 Blurriness Features Image blur due to defocus can be used as another cue for anti-spoofing. Mainly two types of blurriness features denoted as b1 and b2 that in F. Crete [6] and P. Marziliano [7]. In F. Crete [6], blurriness is the difference between the original input and its blurred version. The larger the difference, the lower the blurriness in the original image. In P. Marziliano [7], blurriness is measured based on the average edge width in the input image Chromatic Moment Features The absolute color distribution which is dependent on an illumination and camera variations that propose to invariant features to detect abnormality in spoof faces. Normalized facial images converted from the RGB space into the HSV, which is hue, saturation, and value. Calculate the mean, deviation, and skewness of each channel as a chromatic feature form. Since these three features are equivalent to the these statistical moments in each channel, they referred to as a chromatic moment features. Thus the dimensionality of the chromatic moment feature vector is 5 3 = Color Diversity Features Another difference between genuine and spoof faces is the color diversity. Genuine faces tend to have richer colors. Color quantization is performed on the normalized face image. Two measurements are used from the color distribution: i) the histogram bin counts of the top 100 most frequently appearing colors, and ii) the number of distinct colors which is appearing in the normalized face image. The dimensionality of the color diversity feature vector is 101, these features (specular reflection, blurriness, chromatic moment, and color diversity) are finally concatenated together to form an IDA feature vector with 121 dimensions. (a) Hue (b) Saturation (c) Value Figure. 4: Histograms of chromatic difference between a genuine face and a spoof face 4. CLASSIFICATION METHOD 4.1Neural Classifier A neural classifier which is proposed for modification of the given face detection method. Neural classifiers detect the type of attacks which is printed photo or replayed video. It detect it may spoof or not and also the type of attack. In an IQP, contain three parameters (1) Mean Square Error (MSE) (2) Peak Signal to Noise Ratio (PSNR) and(3) Signal to Noise Ratio. This work includes MSE for checking high values with their gray scale range, in between This work use Gaussian Filter (GF) for image smoothing, which is a high frequency filter and it create a filtered image from an input image. Both 2117

6 of as compared and extract the values of MSE, PSNR, and SNR. This work check, the database features of each training data. Proposed solution includes 21 features mainly. All the gray scale representation converted to their corresponding binary forms due to done one bit representing. Thus each feature is evaluated and stored their corresponding database feature array. Proposed solution using new probabilistic neural network classifier for smooth face detection. IQP include MSE, PSNR, and SNR. 5. EXPERIMENTAL OUTCOME Spoof detection feature vectors are IDA features, IQP features and IDA features used here. The Idiap REPLAY-ATTACK, CASIA FASD (H protocol), and MSU MFSD databases are used for this work. This paper which is used two best methods (IDA+IQP) with neural classifier. Proposed method can be achieving much lower error rate on the same database. It clearly identifies soopf and original image with their type of attack. 6. CONCLUSIONN This paper addresses the problem of face spoof detection, mainly in a cross-database scenario. Motion or texture based features are used some published methods, which propose to perform face spoof detection based on IDA and IQP. Four types of IDA features (specular reflection, blurriness, color moments, and color diversity) and IQP (MSE, PSNR, SNR) have been designed to acquire the image distortion in the spoof face images. A neural classifier consisting of IDA and IQP trained for different spoof attacks. Proposed solution suggest an effective and efficient features (example. through feature transformations C. Zhang [8] ) for using in case studies. Thus a neural classifier and image quality analysis method which is detect the type of attacks. The proposed approach performs much better than the ensemble classification method and significantly outperforms the baseline methods in cross-database scenario. 7. REFERENCES [1] Chingovska, Ivana, André Anjos, and Sébastien Marcel. "On the effectiveness of local binary patterns in face anti-spoofing." Biometrics Special Interest Group (BIOSIG), 2012 BIOSIG-Proceedings of the International Conference of the. IEEE, [2] Zhang, Zhiwei, et al. "Face liveness detection by learning multispectral reflectance distributions." Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on. IEEE, [3] T. de Freitas Pereira, A. Anjos, J. M. De Martino, and S. Marcel, LBP TOP based countermeasure against face spoofing attacks, in Proc. ACCV Workshops, 2012, pp [4] G. Chetty, Biometric liveness checking using multimodal fuzzy fusion, in Proc. IEEE FUZZ, Jul. 2010, pp Software Developer Kit. Pittsburgh Pattern Recognition PittPatt.[Online]. Available: accessed Jan [5] R. T. Tan and K. Ikeuchi, Separating reflection components of textured surfaces using a single image, IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp , Feb

7 [6] F. Crete, T. Dolmiere, P. Ladret, and M. Nicolas, The blur effect: Perception and estimation with a new no-reference perceptual blur metric, Proc. SPIE, vol. 6492, p I, Feb [7] P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, A no-reference perceptual blur metric, in Proc. ICIP, vol , pp. III-57 III-60. [8] C. Zhang, F. Nie, and S. Xiang, A general kernelization framework for learning algorithms based on kernel PCA, Neurocomputing, vol. 73, nos. 4 6, pp , Jan