3DLBP and HAOG fusion for Face Recognition Utilizing Kinect as a 3D Scanner

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1 3DLBP and HAOG fusion for Face Recognition Utilizing Kinect as a 3D Scanner João Baptista Cardia Neto Graduate Program in Computer Science UNESP - São Paulo State University Bauru, São Paulo, joaobcardia@gmail.com Aparecido Nilceu Marana Department of Computing - Faculty of Sciences UNESP - São Paulo State University Bauru, São Paulo, nilceu@fc.unesp.br ABSTRACT Pose and illumination variability are two major problems with D face recognition. Since 3D data is less sensible to illumination changes and can be used to adjust pose variations, it has been adopted to improve performance on face recognition systems. The main problem with utilizing 3D data is the high cost of the traditional 3D scanners. The Kinect is a low cost device that can be used to obtain the 3D data from an environment in a fast manner, but with lower accuracy than the traditional scanners. Recently, a 3D Local Binary Pattern (3DLBP) method was proposed for 3D face recognition by using high resolution scanners. The main goal of this work is to assess the performance of 3DLBP method, fused with Histogram of Averaged Oriented Gradients (HAOG) face descriptor method, for face recognition when Kinect is used as the 3D face scanner. Another goal is to compare the 3DLBP method, fused with HAOG descriptor, with other methods proposed in the literature for face recognition by using Kinect. Experimental results on EURECOM face dataset showed that the data generated by Kinect are discriminative enough to allow face recognition and that 3DLBP performs better than the other methods. Categories and Subject Descriptors I.5.5 [Pattern Recognition]: Applications; I.4.m [Image processing and computer vision]: Miscellaneous biometrics General Terms Pattern Recognition, Image Processing, Computer Vision, Biometrics Keywords 3D Face Recognition, Kinect, 3DLBP, HAOG. 1. INTRODUCTION Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. ACM 15,April 13-17, 015, Salamanca, Spain. Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM /15/04$ Nowadays, the great majority of systems that need to assure a person identity utilizes passwords, a security access card, or a combination of both. Since these kinds of identification are based on something a person knows (password) or has (security card), it is possible to an impostor to learn a needed password or to get the object that grants him the same access of a genuine subject [1]. If someone steal a security card of a person and the access restriction relies only on this object, it becomes impossible to differentiate a genuine subject from an impostor. The same happens when a knowledge, such as a password, is learned. The best way to overcome these drawbacks is utilizing what a person is (his/her biometrics characteristics), instead of what a person has or knows. Biometrics characteristics are the physical/physiological (face, fingerprint, iris, hand veins) or the behavioral (gait, voice, typing dynamics, signature) traits. If a human characteristic satisfies some requirements, then it is possible to utilize it to biometrics recognition [6]. Such requirements are: Universality: The characteristic must occur in as many people as possible; Uniqueness: The characteristic must be different from a person to another; Permanence: The characteristic should not change over time; Collectability: The characteristic must be easily collected; Performance: The characteristic must allow high accuracy, with low processing time and low computational requirements, besides being robust to uncontrolled environment; Acceptability: The subjects agree to be identified by the characteristic; Circumvention: The characteristic must be difficult to circumvent. The Table 1 shows a comparison of some of the most important biometrics characteristics in respect to these requirements. As one can see, face has some advantages over the other characteristics, since it presents high universality, high collectability, and high acceptability. The human

2 beings normally recognize each other trough their face characteristics, which can be obtained at a distance and also in a covert manner [10]. However, this biometrics feature has some disadvantages. For instance, in the D face recognition system the illumination and pose variations can dramatically decrease the system performance. Requirement Face Fingerprint Iris Gait Universality H M H M Uniqueness L H H L Permanence M H H L Collectability H M M H Performance L H H L Acceptability H M L H Circumvention L M H M Table 1: Comparison among some of the most important biometrics characteristics [7]. H = High, M = Medium and L = Low. Since the face is a 3D object, one of the best ways to deal with illumination and pose changes is utilizing a 3D or a.5d representation of the face. However, when dealing with 3D data a problem arises: the high cost of 3D sensors. Table shows the prices and other characteristics of some popular 3D sensors [9]. As one can see, the Kinect arises as a cheap alternative for the expensive 3D devices. Device Speed Charge Size Price Acc. 3dMD sec N/A >$50K <0. Minolta.5 no 1408 >$50K 0.1 Artec Eva no >$0K 0.5 3D3 HDI R1 1.3 no N/A >$10K 0.3 SwissRanger 0.0 no >$5K 10 DAVID SLS.4 no N/A >$K 0.5 Kinect no 41.5 >$ Table : Comparison among different 3D scanners [9]. The speed is expressed in seconds, size in inch 3, price in USD, and the accuracy is an approximation expressed in mm. The Kinect sensor captures depth data of the objects in the environment. For the face recognition task, the depth data can be utilized to deal with pose, illumination, and also facial expression problems. Besides the low cost, Kinect has other advantage: speed. Considering a realistic scenario, it is not feasible to wait.5 seconds in front a device in order to get the face scanned, as required by some sensors. Kinect can scan a person s face in only s. Recently, 3DLBP (3D Local Binary Pattern) method [4] was proposed for 3D face recognition by using high resolution scanners. The main goal of this work is to assess the performance of 3DLBP method, fused with HAOG (Histogram of Averaged Oriented Gradients) face descriptor method [], for face recognition when Kinect is used as the 3D face scanner (the 3DLBP method is described in the subsection 3. and the HAOG method is described in the subsection 3.5). Another goal of our work is to analyze the performance of the 3DLBP method fused with HAOG face descriptor when compared with other methods proposed in the literature for face recognition using Kinect data. Figure 1: The Kinect sensor: 1) Depth Sensor; ) RGB Camera; 3) Microphone array; and 4) Motorized tilt.. KINECT The Microsoft Kinect device contains a depth sensor, a RGB camera, a microphone array, and a motorized tilt. The depth sensor contains an infrared emitter and an infrared camera. Kinect estimates the depth data by projecting an array of infrared dots and measuring the distortion caused by the rays reflected back to the camera [15]. The Kinect sensor outputs a 640 X 480 depth grid with a precision of 11 bits at 30 Hz [18]. The depth range is between 1. and 3.5 meters. The angular field of view of the cameras is 57 horizontally and 43 vertically [16]. These angles are relative to the horizon (and not to the base of the device). It is possible to use the controlled tilt engine to adjust the angle caught in the frame. This way, the device can focus on the desired object. The Figure 1 shows a Kinect sensor. Observing again the Table and comparing the Kinect with others 3D scanners, it is possible to see its main flaw: the low resolution, which brings some new challenges and shows the importance of assessing the performance of face recognition methods proposed in the literature, in general, for high resolution sensors, when using Kinect devices as 3D sensor. 3. METHODS FOR FACE RECOGNITION There are several methods proposed in the literature for face recognition, mainly for D. Amongst these methods, a very few are dedicated to face recognition utilizing Kinect as 3D data source. In this section, some traditional D face recognition, as well as, some more recent 3D face recognition methods are described. 3.1 Local Binary Pattern The Local Binary Pattern (LBP) was originally introduced by [1] and it consists in analyzing the difference between a pixel and its 3X3 neighborhood. Given a central pixel (x c, y c) the LBP operator can be given as [14]: LBP (x c, y c) = 7 s(i n i c) n (1) n=0 with i c as the grey value at the center pixel, i n the value of the neighborhood pixel and s(x) defined as: s(x) = { 1 if x 0; 0 if x < 0. The Figure shows an example of how the code is generated. ()

3 and P 4. The value of P 1 has the same value as the original LBP. For matching, the histograms of the local regions (P 1, P, P 3, P 4) are concatenated. The Figure 4 shows the process for the generation of the 3DLBP face descriptors. Figure : Example of how to calculate the LBP Operator. Later, [13] proposed an extension to the original LBP method. Instead of using a 3X3 neighborhood, the authors proposed to use another neighborhood given by (P, R), in which P is the quantity of points sampled in a circle of radius R. The Figure 3 illustrates a LBP(4,1) neighborhood. If the coordinates of the central pixel are (0, 0) the coordinates of a neighbor g p are given by: ( R sin ( ) πp, R cos P ( )) πp P The values that does not fall in the center of a pixel are estimated by interpolation. (3) Figure 4: The full process of the 3DLBP proposed by [4]. Each of the differences is encoded into the layers (layer, 3 and 4) and the signal into the layer 1. Figure 3: Example of a LBP(4,1), the black dots are the sampling points in the red circle. 3. 3D Local Binary Pattern (3DLBP) The LBP operator takes into consideration only the signal of the comparison between a region and its kernel. The original operator is a powerful feature for texture description, but cannot deal with behavior of depth values. For instance, the nose tip is one of the most reliable landmarks on the face [9]. But, because Kinect data is too noisy, the original LBP method is not able to detect it. Other points in the face will have the same LBP code as the nose tip point. The 3D Local Binary Pattern (3DLBP) operator, proposed by Huang et al. [4] as a variation of the original LBP, considers not only the signal of the difference between a neighbor and its kernel, but also its absolute value. Huang et al. [4] state that more than 93% of all depth differences (DD) within a radius R = are smaller than 7. Due to this property, the absolute value of the DD can be stored into three binary units (i, i 3, i 4). Therefore, it is possible to affirm that: DD = i + i i 4 0 (4) The binary unit i 1 is defined as s(x) (equation ). The four binary units are divided into four layers and, for each layer, four decimal numbers are obtained: P 1, P, P 3, 3.3 Four-Patch Local Binary Pattern (FPLBP) The Four-Patch LBP (FPLBP) [17] is a variation of the original Local Binary Pattern (LBP) operator. It works utilizing two rings of radius r 1 and r centered at a pixel. S patches of size W W are distributed along both rings. The FPLBP codes are produced by comparing two center symmetric patches in the inner rings with two in the outer ring. The code of the pixel is set according to what pair being compared is more similar. This is defined as: S i F P LBP r1,r,s,w,α(p) = [f(d(c 1i, C,1+α mod S) d(c 1,i+ S, C,i+ S +α mod S)) i ] 3.4 Histogram of Oriented Gradients (HOG) In [3] the authors generate entropy and salience maps for the Depth and RGB images. Then, the Histogram of Oriented Gradients (HOG) is applied on the entropy and salience maps. The HOG obtained from different face patches are concatenated in order to compose the facial feature vectors. In this work, the classification of the samples is carried out by a Random Decision Forest (RDF) classifier. 3.5 Histogram of Averaged Oriented Gradients (HAOG) (5)

4 In [], the authors proposed a method for inter-modality Face Sketch Recognition. The main goal of the method is to reduce the gap made by the different modality between face photos and sketches. The method is based on a new gradient orientation descriptor named Histogram of Averaged Oriented Gradients (HAOG). The gap is generated by the difference of visual information that can be seem in a photo and a sketch. A face has several components (e.g. eyes, eyebrows, lips) that have strong relations to each other in a spatial configuration []. The general shape of the face and its spatial configuration are considered the meaningful visual information. With this in mind, it is possible to affirm that the amount of shape information from a photo and a sketch is the same (the face shape is not involved in the modality gap). While the face shape does not generates modality gap, the same cannot be said for the texture. Since texture is related to face appearance, it is deeply related with how the modality gap presents itself. Face appearance has coarse and fine textures, belonging to facial components and facial skin. Boundaries of the facial components with high contrast are coarse textures, while the low contrasts from the face skin (e.g. flaws, moles) are fine textures. It is possible to explore the problem separately for each type of textures. Coarse textures are vital for artists to draw sketches, while fine textures details can be ignored. Since the discriminative power of the extracted orientation gradients are related with the modality gap and only the fine textures are responsible for the gap, it can be concluded that the more robust way to deal with inter-modality recognition is to use only coarse textures for feature extraction. Since it is not feasible to separate both the texture types, the best way to deal with this situation is to use both of them, but emphasizing on the coarse textures. One way to achieve this is to vote square magnitudes of gradient into histogram of orientations. The HAOG descriptor is computed in three main steps. First the grayscale image has a S S window sliding over it, starting in the left upper corner and ending at the right lower corner. For each pixel in a local patch, the ρ and ϕ are defined. For each patch a histogram with b bins is defined. The histogram is quantified by accumulating ρ at the bin in which ϕ fell into. The final descriptor is the concatenation of the histograms of all patches. In order to get ρ and ϕ, it is necessary to calculate first the gradient vector for the image. Given an image in grayscale I(x, y), the gradient vector is defined as: [ ] gx(x, y) = g y(x, y) [ ] I(x,y) x I(x,y) y The values of ρ and ϕ can be calculated with: (6) ρ = (g x + g y) 0.5 (7) ( ) ϕ = tan 1 gy g x The main problem here is that it is unfeasible to calculate directly the averaged oriented gradient, since opposite gradients at both sides of an edge can cancel each other. To deal with this problem [8] proposed to double the gradient angles before averaging. For defining ρ and ϕ first is needed (8) to define the average squared gradient for each pixel in a local neighborhood in a window W, this is done by: [ ] gsx = g sy (9) [ ] ρ cos ϕ ρ = sin ϕ [ ] [ ] ρ (cos ϕ sin ϕ) g ρ = x gy ( sin ϕ cos ϕ) g yg x ] [ ] [ ] [ḡsx = W gsx = W (g x gy) ḡ sy W gsy W gygx Then, ρ and ϕ are defined by: (10) ( ) ϕ = tan 1 ḡsy, ϕ [ π, π) (11) ḡ sy ρ = W (g sy + g sx) 0.5 = W 4. SYMMETRIC FILLING ρ (1) In [9] the authors utilize depth and RGB images to perform face recognition. The RBG image goes through the Discriminant Color Space (DCS) transform before taking part in the method. Since the image has three channels, they are stacked. The depth map used in this work is based on the cloud of points returned by the Kinect. For each cloud of points, the pose is corrected and the Symmetric Filling is applied to them. Symmetric filling is a process in which each point from a mirrored cloud of points is compared with the closest point in the original cloud. If the Euclidean distance between them is smaller than a threshold, the mirrored point is added to the original cloud of points. Due to the symmetrical characteristics of the human face, this strategy tends to improve the data quality generated by the Kinect sensor. In this work, the classification of the samples occurs with the Sparse Representation Classifier (SRC). 5. PROPOSED METHOD In this work we propose the fusion of 3DLBP method with the Histogram of Averaged Oriented Gradients (HAOG) face descriptor method for face recognition when Kinect is used as the 3D face scanner. The Figure 5 shows a diagram of the proposed method. For face normalization, the depth maps generated by the Kinect device have a sphere of radius R cropped centered at the nose tip. This is the only pre-processing applied on the depth images generated by the Kinect. For feature extraction, the cropped area was divided into 8X8 small regions and, for each region, it was applied the 3DLBP operator. The 3DLBP utilize a 3X3 neighborhood. After extracting the operator for all neighborhoods in a region, the values are normalized and a histogram with 14 bins is extracted. Since each neighborhood has four layers, this is done to each one of them. The final face descriptor is the concatenation of each small region histogram. The Figure 6 illustrates the feature extraction with 3DLBP. The HAOG is applied to the same region as the 3DLBP operator. The difference is that it generates a histogram with seven bins. For the classification stage, a Support Vector Machine (SVM) classifier is utilized. For the classification, the gallery

5 Figure 5: Diagram of the proposed method. The fusion occurs in the score level. is the training set and the probe are the test set. Each image in the probe receives a probability of being the subject n. The probe identity is considered the class with higher probability. The fusion between the 3DLBP and HAOG is made at the score level. The final classification score is given by: F S = 3DLBP SC w 1 + HAOGSC w (13) in which F S is the final score, 3DLBP SC is the score from the 3DLBP method, w 1 is the weight for the 3DLBP score, is HAOGSC the score from HAOG classification, and w is the weight for the HAOG classification. 5.1 Generation of new depth maps In our work, in order to increase the robustness of the 3D face recognition from kinect data, we generate another depth map from the cloud of points outputted by Kinect. For the generation of the new depth maps a circle with radius R is cropped centered at the nose. After the cropping stage, each face goes through the symmetric filling process. Then, the face is fitted to a smooth surface using approximation rather than interpolation, this is done with an open Figure 6: Overview of the face feature extraction by using the 3DLBP method. The face is divided 8X8 windows, for each window the 3DLBP is applied in all 3X3 regions. All the histograms for each window is concatenated to form the image descriptor. source code 1 written in MATLAB. The resulting face is a 100 X 100 matrix that is saved to a.bmp file. Since the image file cannot contain decimal values, all the depth values are rounded. Figure 1(b) shows an example of depth map generated by using this method. 6. EXPERIMENTAL RESULTS In order to assess the proposed method, two experiments were carried out on the EURECOM Face Dataset [5, 11]. These experiments, as well as, the database are described in this section. 6.1 EURECOM Face Dataset The EURECOM Face Dataset [5, 11] is composed by 5 subjects, 14 females and 38 males. There are two sets of images captured in the interval of 15 days and the images 1

6 in each set have nine types of variations: neutral, smiling, open mouth, illumination variation, occlusion of the eyes, occlusion of the mouth, occlusion of half of the face, left and right profiles. The Figure 7 shows the images of a subject in the EURECOM Kinect Face dataset. The images have three different source of information: depth (in bitmap depth images and text files with all the values sensed by the Kinect), RGB image, and 3D.obj files. For each sample and for each format there are annotations for the left and right eyes, the tip of the nose, the left and right sides of the mouth and the chin. Figure 7: Images of a subject in the EURECOM Kinect Face dataset. 6. Experiment 1 The first experiment was carried out to compare the results obtained by 3DLBP, HAOG, and by their fusion against the results obtained by [3]. In this experiment only the open mouth, smile, neutral and light on images from both sessions of the EURECOM Kinect Dataset were utilized. Those images are divided into two groups: gallery and probe. The gallery was composed of 75% of subject images available in the sets: open mouth, smile and light on. The neutral set of faces was utilized as probe. The Figure 8 shows an example of the two sets, probe and gallery, of a subject in the EURECOM Face dataset. Figure 9: CMC curves obtained from results utilizing the EURECOM Face dataset. Figure 10: DET curve for the 3DLBP, HAOG, and the fusion between them. The results were obtained utilizing the EURECOM Face dataset. Figure 8: Gallery and probe for a subject in the EURECOM Kinect Face dataset. The Figure 9 shows the Cumulative Match Characteristic (CMC) curves obtained utilizing the EURECOM Face Dataset. The fusion between 3DLBP and HAOG has the higher identification accuracy at Rank 1 and. The 3DLBP method has the higher identification accuracy at Rank 3 and 4, at 5 it ties with the fusion. The HAOG method starts with the worst performance but passes HOG at Rank 5. The Figure 10 shows the DET (Detection Error Tradeoff) curves for this experiment. One can observe that the fusion of the 3DLBP and HAOG methods obtained the best results. The results showed in this paper for all methods, except the 3DLBP, HAOG, and their fusion are the ones published in [3]. 6.3 Experiment The second experiment was carried out to asses the robustness of the face recognition method when dealing with face obstruction. The main goal was to observe the performance gain when utilizing new depth maps generated from the cloud of points after the symmetric filling process. The 3DLBP, HAOG, and their fusion were evaluated on two subsets of the database: one obtained with the original depth maps outputted by the Kinect and the other with the new depth maps obtained from the cloud of points outputted by

7 the Kinect. This experiment has a change in the probe: the face images with the right side occluded were added to the probe set. Another difference in this experiment is that not only the original depth maps from Kinect were utilized, but also the depth maps generated from the cloud of points of each face. The Figure 11 shows the type of faces included in the probe set and the Figure 1 illustrates an example of the original and generated depth maps. Figure 11: Example of face with occlusion utilized in the second experiment. Figure 13: DET curve of two different experiments, one utilizing original depth data and other with regenerated depth maps. The regenerated depth maps are based on point clouds after the symmetrical filling process. Figure 1: Two types of data utilized in the second experiments. a) Depth map outputted directly by the Kinect; and b) Depth map generated from the cloud of points after the symmetric filling. The Figure 13 shows the DET curve comparing the 3DLBP, HAOG, and their fusion in two sets of images: with the original Kinect data and with the new depth maps based on the cloud of points. One can see that the depth data outputted by Kinect obtained better performance than the depth map generated from the cloud of points. However, the fusion between 3DLBP and HAOG with the regenerated depth maps is not very far from it. The images utilized in this experiment are the same as the previous. The Figure 14 shows the same comparison as the previous experiment, but an image with face obstruction is included in the probe. The fusion between 3DLBP and HAOG has the better result when utilizing the new depth images, the other methods have similar performance and only the HAOG with the original data have a very low performance. 7. CONCLUSIONS From the experimental results presented in Section 6, we can conclude that: Figure 14: DET curve of two different experiments, one utilizing original depth data and other with regenerated depth maps. This is done with a image with half face obstructed. The regenerated depth maps are based on point clouds after the symmetrical filling process. 1. The 3DLBP method, besides being effective for 3D face recognition using data captured with high resolution 3D sensors, can also be applied to 3D face recognition using data captured by low cost and low resolution sensors, like the Kinect;. The 3DLBP method, which uses only depth maps, presented better results than the Entropy and Salience map method [3], which uses depth maps and RGB information; 3. Although the HAOG descriptor has the lower perfor-

8 mance in several cases, when fused with 3DLBP it improves its performance, becoming a good alternative to increase face recognition performance; 4. The Symmetric Filling method helps to increase face recognition performance when dealing with face obstruction; 5. The results obtained in this work corroborates that the data generated by Kinect, even though being of lower resolution when compared with other traditional 3D sensors, are discriminative enough to allow a correct face recognition among different subjects. It is important to emphasize that 3DLBP and HAOG methods rely only on the depth maps and do not use the RGB images in any moment. This is important because the depth information can be constructed utilizing the cloud of points provided by the 3D sensors. Therefore, the method can be applied even for non canonical face images. That is, the method is robust to images with variations on pose and with some degree of obstruction. Besides, since the depth data is less sensible to illumination changes than RGB, the 3DLBP and HAOG methods are more robust to these kind of problems. 8. ACKNOWLEDGMENTS We thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the financial support. 9. REFERENCES [1] R. Bolle and S. Pankanti. Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society. Kluwer Academic Publishers, Norwell, MA, USA, [] H. K. Galoogahi and T. Sim. Inter-modality face sketch recognition. 01 IEEE International Conference on Multimedia and Expo, 0:4 9, 01. [3] G. Goswami, S. Bharadwaj, M. Vatsa, and R. Singh. On rgb-d face recognition using kinect. International Conference on Biometrics: Theory, Applications and Systems, 013. [4] Y. Huang, Y. Wang, and T. Tan. Combining statistics of geometrical and correlative features for 3d face recognition. In Proceedings of the British Machine Vision Conference, pages BMVA Press, 006. doi:10.544/c [5] T. Huynh, R. Min, and J.-L. Dugelay. An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data. In ACCV 01, Workshop on Computer Vision with Local Binary Pattern Variants, Daejeon, Korea, November 5-9, 01 / Published also as LNCS, Vol 778, PART 1, Daejeon, KOREA, DEMOCRATIC PEOPLE S REPUBLIC OF, [6] A. K. Jain and D. Maltoni. Handbook of Fingerprint Recognition. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 003. [7] A. K. Jain, A. Ross, and S. Prabhakar. An introduction to biometric recognition. IEEE Trans. on Circuits and Systems for Video Technology, 14:4 0, 004. [8] M. Kass and A. Witkin. Analyzing oriented patterns. Comput. Vision Graph. Image Process., 37(3):36 385, Mar [9] B. Li, A. Mian, W. Liu, and A. Krishna. Using kinect for face recognition under varying poses, expressions, illumination and disguise. In Applications of Computer Vision (WACV), 013 IEEE Workshop on, pages , 013. [10] S. Z. Li and A. K. Jain, editors. Handbook of Face Recognition, nd Edition. Springer, 011. [11] R. Min, N. Kose, and J.-L. Dugelay. Kinectfacedb: A kinect database for face recognition. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, PP(99):1 1, 014. [1] T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 9(1):51 59, [13] T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 4(7): , 00. [14] Y. Rodriguez and S. Marcel. Face authentication using adapted local binary pattern histograms. In 9th European Conference on Computer Vision (ECCV), IDIAP-RR [15] A. Shpunt and Z. Zalevsky. Depth-varying light fields for three dimensional sensing, 008. US Patent App. 11/74,068. [16] J. Stowers, M. Hayes, and A. Bainbridge-Smith. Altitude control of a quadrotor helicopter using depth map from microsoft kinect sensor. In Mechatronics (ICM), 011 IEEE International Conference on, pages , april 011. [17] L. Wolf, T. Hassner, and Y. Taigman. Y.: Descriptor based methods in the wild. In In: Faces in Real-Life Images Workshop in ECCV. (008) (b) Similarity Scores based on Background Samples. [18] M. ZollhÖfer, M. Martinek, G. Greiner, M. Stamminger, and J. Sussmuth. Automatic reconstruction of personalized avatars from 3d face scans. Comput. Animat. Virtual Worlds, (-3):195 0, Apr. 011.