Logical Conjunction of Triple-perpendicular-
directional Translation Residual for Contactless
Palmprint Preprocessing
Lu L...
based method [11], feature-point-based method [12],
maximum-inscribed-circle-based method [13], and so on.
Unfortunately, ...
Assume the values of the element in Cb and Cr channels are
Cb(i,j) and Cr(i,j), respectively. i and j denote the row and
c...
Figure 2. Four final valley points.
(Xk,Yk) is the centroid of k-th region computed by:
1 1
,
k kn n
k k
i i
i i
k k
k k
x...
(a) (b)
(c) (d) (e)
(f) (g) (h)
Figure 5. Comparison of location accuracy: (a) Original image; (b) Binary
palm image; (c) ...
TABLE III. COMPARISON OF VERIFICATION PERFORMANCE
CHVD TPDTR
Accuracy 99.8% 100%
μ1 0.2203 0.2063
μ2 0.4476 0.4476
σ1 0.00...
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Logical Conjunction of Triple-perpendiculardirectional Translation Residual for Contactless Palmprint Preprocessing (52)

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Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendiculardirectional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR; therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy.

Proceedings of The International Conference on Information Technology: New Generations, USA.

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Logical Conjunction of Triple-perpendiculardirectional Translation Residual for Contactless Palmprint Preprocessing (52)

  1. 1. Logical Conjunction of Triple-perpendicular- directional Translation Residual for Contactless Palmprint Preprocessing Lu Leng, Gang Liu, Ming Li Key Laboratory of Nondestructive Test (Ministry of Education) Nanchang Hangkong University Nanchang, P.R.China leng@nchu.edu.cn, liugang641@gmail.com, liming@nchu.edu.cn Muhammad Khurram Khan Center of Excellence in Information Assurance King Saud University Riyadh 11653, Saudi Arabia mkhurram@ksu.edu.sa Ali M. Al-Khouri Emirates Identity Authority United Arab Emirates Abstract—Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendicular- directional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR; therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy. Keywords-contactless biometrics recognition; logical conjunction; triple-perpendicular-directional translation residual; valley point detection; location of region-of-interest I. INTRODUCTION Biometric refers to humans’ physiological or behavioral characteristic, which is more reliable for identity recognition/verification than possession-based and knowledge- based methods [1]. Palmprint, as a relatively new biometric, has several superiorities over other biometrics [2, 3]. The rich and stable palmprint features can achieve high accuracy performance. Besides, it can be easily captured by acquisition systems with low cost. In addition, the user acceptance of palmprint is high. Due to the several advantages of palmprint, it has been widely used for identity authentication [4]. Currently, palmprint acquisition systems can be categorized into contact-type [5] and contactless-type [6]. In contact acquisition, the background of the acquired palmprint image is stable and the position of the palm is fixed [7]. Furthermore, the background is controlled so that it is easy to segment the hand region and locate the region-of-interest (ROI) [8]. Although contact palmprint recognition systems can achieve high accuracy performance, some problems occur in the practical application as follows. (1) Personal hygiene: Due to the health and personal safety, it is unhygienic to make the users’ fingerprints or palms contact the identical sensor or devices for verification, which increases the risk of infectious diseases. (2) Lack of acquisition flexibility: The user acceptance is reduced by the fixing devices that degrade acquisition flexibility and convenience. (3) Surface contamination: The surface of contact sensors in some acquisition systems will get contaminated easily especially in harsh, dirty, and outdoor environments. The surface contamination of contact sensors is likely to degrade the quality of the following acquired palmprint images. (4) Resistance of customs: Some conservative nations resist placing their hands on the device that is touched by the users of the opposite sex. Thus the research on palmprint recognition system has been toward contactless-type gradually [9, 10]. Contactless palmprint recognition systems are significant and a large number of researchers devote themselves to the preprocessing of contactless palmprint images. Preprocessing is the prerequisite of palmprint recognition. The traditional preprocessing of palmprint includes hand segmentation, valley detection and ROI location. Some preprocessing methods for contact palmprint recognition systems were proposed, such as principal-line- This work was partially supported by NPST Program by King Saud University (13-INF943-02), National Natural Science Foundation of China (61305010, 61262019, 61202112, 61303199), China Postdoctoral Science Foundation (2013M531554), and Doctoral Starting Foundation of Nanchang Hangkong University (EA201308058), International Postdoctoral Exchange Fellowship Program of China.
  2. 2. based method [11], feature-point-based method [12], maximum-inscribed-circle-based method [13], and so on. Unfortunately, the aforementioned methods cannot be directly used for the preprocessing of contactless palmprint images due to the severe challenges as follows. (1) Position of hand: The appropriate positions of hand placement are different in different contactless palmprint systems. However, users can place their hands freely. If the hand is too far, palmprint details will be lost; while if the hand is too close, it is probable that some parts of palm are not captured. Besides distance, the hand can be translated, rotated and revolved without any restriction. Therefore, it is difficult to locate ROI of contactless palmprint image. (2) Interference of complex background: There are many skin-like regions in the complex background. The complexity of background in unrestricted environment increases the difficulty of hand segmentation. (3) Unstable illumination: The light of the palmprint acquisition cannot be rigidly controlled in open environment; therefore, the preprocessing of contactless palmprint image should cope with the light disturbance, like light intensity, light color. Although the development of contactless palmprint recognition system is still in its infancy, some preprocess methods of contactless palmprint image have been reported in the literatures. Competitive hand valley detection (CHVD) was proposed to locate the ROI of the palm [6]. CHVD, as a popular ROI location algorithm, was then used in [14] for the preprocessing of contactless palmprint image. However, the premise of CHVD is that the hand region can be accurately segmented from the background; otherwise, the inaccurate boundary of hand region results in false valley point detection. In [15], skin color modeling was improved with active shape model (ASM) [16]. A statistical model of the global shape is built in ASM to represent a parametric deformable model. However, ASM relies on the geometry that is sensitive to interference of complex background. Active appearance model (AAM) was proposed in unrestricted posture and background to improve the efficiency, accuracy and robustness [17]. AAM forms a statistical model of shape and texture together, so the computational cost is high. Because of the above challenges of contactless palmprint preprocess, this paper proposes a fast and accurate processing algorithm, namely logical conjunction of triple-perpendicular- directional translation residual (TPDTR), to improve the preprocessing of contactless palmprint image. The advantages of the proposed algorithm include: (1) Reduction of computational cost The processing capacity in some contactless palmprint verification systems, e.g. smart card and radio frequency identification (RFID), is limited, so it is necessary to reduce the computational cost [18]. The search of hand valley point is within the borders of hand valley gap between fingers, which are detected by TPDTR; therefore, the computational cost is effectively decreased. (2) Anti-interference capacity The centroids of four regions of candidate valley points are computed as the final hand valley points. Since the anti- interference capacity of region is stronger than that of point and line, the proposed algorithm improves the accuracy of hand valley point detection. The rest of this paper is organized as follows: Section II presents the proposed the methodology of preprocessing of contactless palm image. Section III describes the experimental results. The conclusions are drawn in Section IV. II. METHODOLOGY The preprocess of palmprint system consists of three steps. First, skin-color thresholding method segments hand from the background. After that, TPDTR is used to detect the borders of hand valley gap between fingers. Finally, ROI is located dynamically according to the distance between two selected valley points. The results of the steps of the proposed preprocessing algorithm are shown in Fig. 1. A. Skin-color Thresholding Skin-color model is used to segment hand from the background. The RGB color space is not suitable for skin-color model. In order to overcome illumination disturbance, RGB color space is converted to YCbCr color space, in which color and brightness are separated. Besides, human skin colors have obvious clustering characteristics in YCbCr color space [19]. (a) (b) (c) (d) (e) (f) (g) (h) (i) Figure 1. Results of the steps of the proposed preprocessing algorithm: (a) Original image; (b) Skin color likelihood image; (c) Binary palm image; (d) Residual of upward translated image; (e) Residual of left translated image; (f) Residual of right translated image; (g) Logical conjunction of TPDTR; (h) Borders of hand valley gap; and (i) Four regions of candidate valley points.
  3. 3. Assume the values of the element in Cb and Cr channels are Cb(i,j) and Cr(i,j), respectively. i and j denote the row and column of the element, respectively. 1≤i≤h, 1≤j≤w, h and w are the height and width of palmprint image, respectively. The human skin color can be modeled as a Gaussian distribution, so the likelihood of Cb-Cr of the element is: ( ) ( )( ) ( )( )1 , exp 0.5 , , T i j i j i j−⎡ ⎤= − − − ⎣ ⎦ Li c μ σ c μ (1) where c(i,j)=[Cb(i,j) Cr(i,j)], μ and σ are the mean vector and covariance matrix of Cb-Cr joint distribution, respectively, which are determined by a large number of samples. The original palmprint image is shown in Fig. 1(a). The likelihood image Li in Fig. 1(b) is thresholded to be the binary palmprint image L in Fig. 1(c), in which the white region labels the segmented hand region. B. Valley Point Detection Based on TPDTR TPDTR is used to detect the borders of hand valley gap between fingers, in which the points are checked by three conditions to search candidate valley point. Step 1. Translate L along three perpendicular directions (up, left, right) by a pixel to construct three translated binary palm images: ( ) ( ), 1 ,1 , 0 u u i a j i h a j w i j otherwise ⎧ + ≤ ≤ − ≤ ≤ = ⎨ ⎩ L L (2) ( ) ( ), 1 ,1 , 0 l l i j a i h j w a i j otherwise ⎧ + ≤ ≤ ≤ ≤ − = ⎨ ⎩ L L (3) ( ) ( ), 1 , 1 , 0 r r i j a i h a j w i j otherwise ⎧ − ≤ ≤ + ≤ ≤ = ⎨ ⎩ L L (4) Step 2. The three residual images along three perpendicular directions, shown in Fig. 1(d)(e)(f), are computed by: ( ) ( ) ( )1 , , , 0 u ur i j i j i j otherwise ⎧ > = ⎨ ⎩ L L L (5) ( ) ( ) ( )1 , , , 0 l lr i j i j i j otherwise ⎧ > = ⎨ ⎩ L L L (6) ( ) ( ) ( )1 , , , 0 r rr i j i j i j otherwise ⎧ > = ⎨ ⎩ L L L (7) Step 3. L3r, the logical conjunction of TPDTR in Fig. 1(g), is computed by: ( ) ( ) ( ) ( )3 , = , & , & ,r ur lr rri j i j i j i jL L L L (8) where & denotes logical conjunction. L3r detects the four hand valley gaps between five fingers. Four regions, which are larger than the other regions in L3r, are kept in order to avoid the interference regions. Step 4. Translate L3r(i,j) down by b pixels. ( ) ( )3 , 1 ,1 , 0 r er i b j b i h j w i j otherwise ⎧ − + ≤ ≤ ≤ ≤ = ⎨ ⎩ L L (9) Step 5. Lb, the borders of hand valley gap in Fig. 1(h), is the logical conjunction of Ler and L. ( ) ( ) ( ), = , & ,b eri j i j i jL L L (10) Four regions, which are larger than the other regions in Lb, are kept in order to avoid the interference regions. Step 6. We use the conditions in [6] to check whether the points in the four borders of hand valley gap are candidate valley points. When a point simultaneously satisfies the three conditions, this point is considered as a candidate valley point. Condition 1 (Four-point check): Four checking-points are placed α pixels away from the current point along four directions (up, down, left, right). According to the prior knowledge that the position of the hand, we modify Condition 1 with the help of direction information. If the value of the up point is 0, and the values of the other three points are 1, then this point satisfies Condition 1. Condition 2 (Eight-point check): Eight checking-points are placed α+β pixels away from the current point along eight directions. If at least one and not more than four values of the points are 0, while the values of the remaining points are 1, then this point satisfies Condition 2. Condition 3 (Sixteen-point check): Sixteen checking-points are placed α+β+γ pixels away from the current point along sixteen directions. If there is at least one and not more than seven values of the points are 0, while the values of the remaining points are 1, this point satisfies Condition 3. Unfortunately, in each borders of hand valley gap, more than one point simultaneously satisfy the above three checking conditions and are considered as candidate valley points. The candidate valley points construct four regions shown in Fig. 1(i). Step 7. (xi k , yi k )(i=1,2,…,nk) denotes the coordinate of i-th point in k-th region of candidate valley points. The point of (xi k ,yi k ) is in xi k -th row and yi k -th column of k-th region. nk denotes the amount of the points in k-th region. The centroids of the four regions are considered as the four final valley points shown in Fig. 2.
  4. 4. Figure 2. Four final valley points. (Xk,Yk) is the centroid of k-th region computed by: 1 1 , k kn n k k i i i i k k k k x y X Y n n = = = = ∑ ∑ (11) Step 8. Four final valley points from left to right are denoted as P1, P2, P3 and P4, respectively. (Xk,Yk) is the coordinate of Pk. The following rules determine the left or right hands, shown in Fig. 3. Left-hand determination: X1>X2 & X1>X3 & X1>X4 (12) Right-hand determination: X4>X1 & X4>X2 & X4>X3 (13) C. ROI Location Select two valley points (P2 and P4 for left hand, P1 and P3 for right hand). Fig. 4 shows how the ROI of the right-hand is located. The distance between P1 and P3 is m, that is, P1P3=m. 1 2 1 3Q Q PP⊥ , Q1P1=0.2m. The square Q1Q2Q3Q4, with the side length of m, is the located ROI of the right hand. Similarly, the ROI of the left-hand can be also located. III. EXPERIMENTAL RESULTS Experimental setup is shown in Table I. In order to ensure the stability of the algorithm, through experiment analysis, a, b are set to 30 and 5, respectively; while α, β, γ are all set to 5, respectively. The palmprint database of Multimedia University [6] is used for evaluation. In this database, the palmprints of 136 individuals were captured with visible webcams in contactless environment. The users were from different countries, such as China, Malaysia, India, Africa, and so on. About ten samples were captured from each hand for each user. Due to the loss of some samples, we picked two thousand samples from the database. The size of the palmprint is 640×480. Figure 3. Left and right hand determination: (a) Left-hand determination; and (b) Right-hand determination. Figure 4. ROI location of right hand. TABLE I. EXPERIMENTAL SETUP Setup Parameters Operating system Windows XP CPU Intel Pentium 4 (1.60 GHz) Memory capacity 1024 MB Software MATLAB 7.1 Since CHVD is a popular preprocessing algorithm of contactless palmprint image, the experiments compare TPDTR and CHVD in terms of location accuracy, computation cost and verification performance. A. Location Accuracy Due to the several challenges in contactless palmprint recognition systems, the segmented hand region is not always complete, or some non-hand regions are also segmented and merged into hand region. CHVD relies on the quality of the hand segmentation; however, the hand segmentation is not always accurate due to the difference of skin colors and the complexity of background. On the contrary, TPDTR can tolerate the error of hand segmentation.
  5. 5. (a) (b) (c) (d) (e) (f) (g) (h) Figure 5. Comparison of location accuracy: (a) Original image; (b) Binary palm image; (c) Edge of CHVD; (d) Valley points of CHVD; (e) ROI of CHVD; (f) Logical conjunction of TPDTR; (g) Final valley points of TPDTR; and (h) ROI of TPDTR. (a) (b) (c) Figure 6. Comparison in other instances: (a) Original contactless palmprint images; (b) ROIof CHVD; and (c) ROI of TPDTR. Fig. 5 compares the location accuracy of CHVD and TPDTR. In Fig. 5(c), besides the real hand contour points, there are many false contour points caused by complex background, which leads to the failure of CHVD. In Fig. 5(f), the search of valley point is within valid regions of hand valley gaps between fingers, so TPDTR is more robust against false contour. Therefore, location accuracy of TPDTR is higher than that of CHVD. Fig. 6 comparers CHVD and TPDTR in other instances. B. Computation Cost In CHVD, the contour is extracted by edge detection. In edge detection, the pixels in the neighborhood of each pixel are multiplied by the entries of the mask, and then the products are summed. Finally, the sums are converted into bits with a threshold by a judgment. All contour points have to be checked by the three conditions. Thus the computation cost is large. On the contrary, in TPDTR, only simple logical operations and judgments are implemented on binary data. Besides, only the points in the four borders of hand valley gap are checked by the three conditions. The amount of the points in the four borders is less than that of all contour points. Thus the computation cost is reduced obviously. Table II compares the computation cost. The execution time cost of CHVD is the preprocessing from binary palm image generation to edge detection; while the execution time cost of TPDTR is the preprocessing from binary palm image generation to the generation of four regions of candidate valley points. The execution time of TPDTR is less than that of CHVD. In CHVD, all contour points need to be checked; while in TPDTR, only the points in the four borders of hand valley gap need to be checked. Thus the amount of checking points in TPDTR is much less. TABLE II. COMPARISON OF COMPUTATION COST For one sample CHVD TPDTR Execution time 1.2885s 0.96583s Average amount of checking points 3560 550 C. Verification Performance The PalmCodes [5], which are generated from the ROI located by CHVD and TPDTR, are compared in term of verification performance. d' measures how well the genuine and impostor distributions are separated. ( ) 1 2 2 2 1 2 2 d μ μ σ σ − ′ = + (14) where μ1 and μ2 denote the means of the genuine and imposter distributions, respectively; σ1 and σ2 denote the standard deviations of the genuine and imposter distributions, respectively. Large d' implies high separation between genuine and impostor distributions. The values of the parameters in (14) are compared in Table III. μ2, σ1 and σ2 of the two algorithms are similar. μ1 of TPDTR is smaller than that of CHVD, d' of TPDTR is accordingly larger than that of CHVD.
  6. 6. TABLE III. COMPARISON OF VERIFICATION PERFORMANCE CHVD TPDTR Accuracy 99.8% 100% μ1 0.2203 0.2063 μ2 0.4476 0.4476 σ1 0.0036 0.0032 σ2 0.000398 0.000379 d' 5.0839 5.7034 0 0.1 0.2 0.3 0.4 0.5 0 2 4 6 8 10 12 Normalized Hamming distance Percentage(%) Genuine(TPDTR) Imposter(TPDTR) Genuine(CHVD) Imposter(CHVD) Figure 7. Distribution comparison. 10 -6 10 -4 10 -2 10 0 10 2 94 96 98 100 False Accept Rate(%) FalseRejectRate(%) TPDTR CHVD Figure 8. ROC comparison. The genuine and imposter distributions of two algorithms are plotted in Fig. 7. The imposter distributions of two algorithms are similar; while the genuine distribution of TPDTR is on the left of that of CHVD, so μ1 of TPDTR is smaller than that of CHVD, which is coincident with Table III. Receiver operating characteristic (ROC) curves of two algorithms are plotted in Fig. 8. The ROC curve of TPDTR is higher than that of CHVD, so the verification performance of TPDTR outperforms that of CHVD. IV. CONCLUSIONS This paper presents a novel ROI location algorithm of contactless palmprint, namely TPDTR. The checking of hand valley point is performed in the borders of hand valley gap that are detected by TPDTR; therefore, the computational cost is effectively decreased and the accuracy is improved. The experimental results confirm the advantages of TPDTR in location accuracy and computation cost. ACKNOWLEDGMENT The authors would like to express their sincere thanks to the editor and anonymous reviewers for their comments, which significantly helped to improve this paper. The authors would also like to thank Multimedia University in Melaka, Malaysia, for providing us with the palmprint and palmvein databases. REFERENCES [1] M. K. Khan, J. S. Zhang, and K. Alghathbar, “Challenge-response-based biometric image scrambling for secure personal identification,” Future Gener. Comp. Syst., vol. 27, no. 4, pp. 411–418, April 2011. [2] A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,” Patt. Recogn., vol. 42, no. 7, pp. 1408–1418, July 2009. [3] A. B. J. Teoh and L. Leng, Palmprint matching, (in Encyclopedia of Biometrics), 2nd ed, Springer-Verlag Publisher, in press. [4] L. Leng, J. S. Zhang, M. K. Khan, X. Chen, and K. Alghathbar, “Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain,” Int. J. Phys. Sci.,vol. 5, no. 17, pp. 2543–2554, December 2010. [5] D. Zhang, A. W. K. Kong, J. You, M. Wong, “Online palmprint identification,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 25, no. 9, pp. 1041–1050, September 2003. [6] G. K. O. Michael, T. Connie, and A. B. J. Teoh, “Touch-less palmprint biometrics: novel design and implementation,” Imag. Vis. Comp., vol. 26, no. 12, pp. 1551–1560, December 2008. [7] L. Leng, J. S. Zhang, M. K. Khan, X. Chen, M. Ji, and K. Alghathbar, “Cancelable PalmCode generated from randomized Gabor filters for palmprint template protection,” Sci. Res. Essays, vol. 6, no. 4. pp. 784– 792, February 2011. [8] L. Leng and J. S. Zhang, “PalmHash Code vs. PalmPhasor Code,” Neurocomputing, vol. 108, pp. 1–12, May 2013. [9] J. Doublet, M. Revenu, and O. Lepetit, “Robust grayscale distribution estimation for contactless palmprint recognition,” 1st IEEE Int. Conf. Biometrics: Theory, Applications, and Systems, pp. 1–6, September 2007. [10] A. Poinsot, F. Yang, and M. Paindavoine, “Small sample biometric recognition based on palmprint and face fusion,” 4th Int. Multi-Conf. Comp. Global Inf. Techn., pp. 118–112, August 2009. [11] M. Li, C. H. Yan, and G. H. Liu, “Palmprint identification system based on image analysis,” Chinese J. Imag. Graph., vol. 5, no. 2, pp. 134–137, February 2000. [12] Q. Y. Dai, Y. L. Yu, and D. P. Zhang, “Detection and location in palmprint identification system,” J. Guangdong Techn.Univ., vol. 19, no. 1, pp. 1–6, 2002. [13] W. X. Li, S. X. Xia, D. P. Zhang, and Z. Q. Xu, “A new palmprint recognition method based on the characteristics of bidirectional matching line,”. Res. Devel. Comp., vol. 41, no. 6, pp. 997–1002, June 2004. [14] M. Franzgrote, C. Borg, B. J. Tobias Ries, S. Bussemaker, X. Y. Jiang, M. Fieleser, and D. Zhang, “Palmprint verification on mobile phones using accelerated Competitive Code,” Int. Conf. Hand-Based Biometrics, pp. 1–6, November 2011. [15] J. Doublet, O. Lepetit, and M. Revenu, “Contactless hand recognition using shape and texture features,” Int. Conf. Sign. Proc., vol. 3, pp. 1–4, 2006. [16] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models: their training and application,” Comp. Vis. Imag. Underst., vol. 61, no. 1, pp. 38–59, January 1995. [17] M. Aykut and M. Ekinci, “AAM-based palm segmentation in unrestricted posture and background for palmprint recognition,” Patt. Recogn. Lett., vol. 34, no. 9, pp. 955–962, July 2013. [18] M. K. Khan and J. S. Zhang, “Multimodal face and fingerprint biometrics authentication on space-limited tokens,” Neurocomputing, vol. 71, no. 13–15, pp. 3026–3031, August, 2008. [19] P. Kakumanu, S. Makrogiannis, and N. Bourbakis, “A survey of skin- color modeling and detection methods,” Patt. Recogn., vol. 40, no. 3, pp. 1106–1122, March 2007.

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