Journal of World s Electrical Engineering and Technology J. World. Elect. Eng. Tech. 1(1): 12-16, 2012
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1 2011, Scienceline Publication Jounal of Wold s Electical Engineeing and Technology J. Wold. Elect. Eng. Tech. 1(1): 12-16, 2012 JWEET An Efficient Algoithm fo Lip Segmentation in Colo Face Images Based on Local Infomation Hashem Kalbkhani*, Mehdi Chehel Amiani Depatment of Electical Engineeing, Umia Univesity, Umia, Ian *Coesponding autho's st_h.kalbkhani@umia.ac.i Abstact Lip detection is used in many applications such as face detection and lips eading. In pevious woks, eseaches have consideed whole of face image fo lip detection. In this pape we popose a new algoithm. In ou algoithm fo educing equied calculation and incease accuacy of coect detection, we do not conside whole of the face image. We fist emove the uppe half pat of the face image. Then, fo estimate lip aea, we divide emained lowe half face image to equal pats. Fo each pat we calculate statistical infomation such as standad deviation, and based on them we detect lip aea in face image. Fo sepaate lip pixels fom skin pixels, we use YCbC and HSI colo spaces at this wok. We evaluate ou wok on CVL face database. Ou expeiments show that new algoithm gives bette esults than pevious woks on this database. Keywods: lip detection, skin, satuation, standad deviation. INTRODUCTION Lip aea detection and extacting it fom face is impotant in many applications such as face detection, lip eading, and voice detection [1]. In face detection lip is used fo veifying detected aea. The goal of lip-eading pocess is to make natual connection between human and compute. This equied obust algoithm that is esistant in diffeent light, diffeent skin colo and peson-independent. Lip detection is a citical pepocessing step in many humanoiented applications, such as speech ecognition and dental application [2]. The goal is to exploit the facial visual infomation of lip movements that contain the image infomation of speech. Thee ae seveal techniques fo lip aea detection. A lage categoy of techniques ae model-based. In these techniques, at the fist, a model of the face is built. Then the stuctue of lip aea is descibed by a set of model paametes [3]. These techniques include snakes, active contou models and seveal othe paametic models. The most advantage of these systems is that the impotant featues ae epesented in a low-dimensional paamete space and the calculation complexity is deceased. Also, these systems ae good pefomance in conditions such as otation, scaling and illumination vaiation. These models have some poblems. Fo example sometimes lage taining set is needed to cove the high vaiability ange of lips. Fo lip detection seveal colo spaces and segmentation techniques wee used. Fo example, Gomez et al [4] have used components of RGB colo space to ceate new image by a linea combination of ed, geen and blue colos. Hsu et al [5] have used YCbC colo space to ceate new image by non-linea combination of Cb and C values. Nasii et al [2] have used PSO appoach to obtain optimized map fo extacting lip aea in face image. In this pape, fo lip detection we use the enhanced vesion of Lip-Map that poposed in [5]. Fo bette sepaation between lip and skin pixels, we multiply Lip- Map by satuation component of HSI colo space. In ode to educe equied calculation, we fist emove uppe half of the face image. Afte this step, we estimate the lip aea. Fo this pupose, we divide emaining lowe half pat of into some pats and calculate standad deviation fo each pat. Based on standad deviation of each pat, we detemine lip aea. Finally, fo extact lip pixels fom skin pixels, we obtain optimum theshold value to convet the gay scale image into binay image. White pixels in binay image ae lip egion. The est of this pape is oganized as follows: section 2, descibes ou lip detection algoithm. In section 3, we To cite this pape: Kalbkhani H, Chehel Amiani. M An Efficient Algoithm fo Lip Segmentation in Colo Face Images Based on Local Infomation. J. Wold Elect. Eng. Tech. 1(1):
2 J. Wold. Elect. Eng. Tech. 1(1): 12-16, 2012 pesent ou expeimental esults on CVL face database. And finally, section 4 concludes this pape. PROPSED LIP DETECTION ALGORITHEM At the fist step fo lip detection, we must choose efficient colo spaces. In these colo spaces lip must be sepaable fom the skin. Accoding to pevious woks we choose YCbC colo space and fo bette sepaation, we popose to use HSI colo space. Fom these colo spaces, we choose Cb, C and Satuation components fo ou wok. o complexity eduction and bette detection of lip aea, we emove the uppe half of the face image, because we know that the lip is placed at the lowe half pat of the face aea. Then, we popose to divide emaining image into some equal pats and seach fo lip. Finally by thesholding appoach, we sepaate the lip pixels fom the skin pixels. In the following, we intoduce these steps in details. Get Face Image Remove uppe half of face image Ceate EnLip-Map Divide image to equal pats Calculate standad deviation of each pat Detemine lip aea Obtain optimum theshold value Convet gay-scale image to binay image Fig. 1 Block diagam of ou algoithm. A. Choose efficient colo space Accoding to the aangement of the facial components, we know that the lip aea is placed at the lowe half pat of the face aea. So that, in ode to eduction calculation ate, and incease the speed of detection, we popose to emove the uppe half of the face image. Then we keep the lowe half of the face aea; and apply the emaining of ou algoithm on this pat of face image. Hsu et al [5] have poposed a method fo lip detection based on YCbC colo space. Pixels of lip aea have stonge ed component and weake blue component than othe facial egions. Theefoe, the chominance component C has geate value than the Cb in the lip egion. Also, lip egion has elatively low C/Cb value, but it has high C 2 value. Then, Lip-Map is constucted as follows: Lip Map C ( C ( C / C )) (1) b 2 C 0.95 (2) C C b whee both C 2 and C/Cb ae nomalized to the ange [0, 1]. We also nomalize Lip-Map to the ange [0, 1]. The paamete is defined as a atio of aveage C 2 to the aveage C/Cb. The Lip-Map can be emphasizes the lip pixels well. But sometimes in a kind of the people, skin pixels have moe common chaacteistics as lip pixels. This may degade the pefomance of lip pixels segmentation. In Fig. 2, two images ae pesented, that in the ight image sepaation between skin and lip is good, and in the left one sepaation is degaded. So, fo bette sepaation, we tested othe colo spaces. We seached fo spaces that diffeent between lip and face pixels is consideable. Finally, we choose satuation component of HSI colo space. Fo bette sepaation, satuation component is multiplied with Lip-Map. So, enhanced Lip-Map is obtained by bellow equation. EnLip Map S C C C C (3) { ( ( / b)) } Fig. 3 shows EnLip-Map of two images in Fig. 2. B. Estimate lip aea Befoe applying thesholding appoach, we estimate the lip location in the face image. Accoding to the face and lip size, we popose to segment the lowe half pat of the face image in two diections. 13
3 Kalbkhani and Chehel Amiani Fig. 2 Oiginal face images of diffeent people; Lip-Map of oiginal images, espectively. C. Sepaate lip pixels fom skin pixels Afte detemining the lip aea, we must extact lip pixels fom the skin pixels. To enhance the gay-scale EnLip- Map and impove the sepaation, we popose to apply the Top-Hat mophological opeation [6] with ball stuctuing element. The Top-Hat tansfomation of a gay-scale image f with stuctuing element b is defined as f minus its opening: T ( f ) f ( f b) (4) hat whee demonstates opening opeation. Afte this step, we use theasholding appoach. In any theasholding appoach, we need to the theshold value fo compae gay-scale image with this theshold value. And then convet the gay-scale image into binay one. To obtain the optimum theshold value (Th), we use the Otsu s method [6]. Fig. 3 The Enhanced Lip-Map of images in Fig. 2. We fist segment it into thee equal pats accoding to x- axis diection. Then fo each pat, we calculate the standad deviation of the pixels of EnLip-Map. Lip in x- axis diection is placed in the pat that has the maximum standad deviation. Also, we segment the lowe half pat of the face image into thee equal pats accoding to y- axis diection. Then fo each segment, we calculate the standad deviation of the pixels of EnLip-Map. Lip in y- axis diection is placed in the pat that has the maximum standad deviation. Accoding to the segmentation of image into thee equal pats in x and y-axis diection, we have nine blocks. Accoding to the two segments (in x-axis and y-axis) that have maximum standad deviation, we choose one of the nine blocks as lip aea. But, sometimes whole of the lip is not placed into one block. So that, we popose to conside the half of the blocks that ae in the neighboing of the block, which obtained peviously. In this manne, we obtain lip aea in the face image. Fig. 4 illustates these steps. (c) Fig. 4 Segments in x-axis diection, standad deviations fom up to bottom ae , , and ; segments in y-axis diection, standad deviations fom left to ight ae , and ; (c) selected block accoding to the standad deviations; (d) lip aea. The method is optimum, because it maximizes the between-class vaiance, a well-known measue used in statistical disciminant analysis. The impotant popety of this method is that based entiely on computations pefomed on the histogam of an image, that histogam is an easily obtainable 1-D aay. Afte obtaining the optimum theshold value, we convet the EnLip-Map to binay image (Bin-Lip) as follows: 1 Bin Lip 0 (d) if EnLip Map Th if EnLip Map Th (5) 14
4 J. Wold. Elect. Eng. Tech. 1(1): 12-16, 2012 Fo noise eduction, afte apply theshold, we emove connected components that have vey low numbe of white pixels. Fig. 5 shows the binay image of EnLip-Map in Fig. 4. RESULTS Fo evaluation the pefomance of ou new algoithm, we apply it on the images of CVL face database [7]. This database is consists of images fom 114 pesons. Thee ae 7 images fo each peson. These images ae labeled by side view that includes fa left, angle 45, angle 135 and fa ight, fontal view that includes seious expession, smile (showing no teeth) and smile (showing teeth). These images ae taken unde unifom illumination and no flash and pojection sceen ae in the backgound. All of images ae size of pixels and have JPEG fomat. To evaluate the new method, we choose images that have fontal view. In TABLE 1 we have pesented the esults of ou lip aea detection and lip segmentation method. These esults indicated that ou algoithm can find lip aea efficiently. If lip aea is found popely, the sepaation between lip and skin is enhanced. The aveage ate of coect lip aea detection is 98.76%. The pefomance of ou algoithm is compaed with the method of [2] and [5]. Results indicate that ou algoithm has bette efficiency than pevious algoithms. Adding the effect of satuation component and detemining lip aea befoe segmentation emove othe edundant egions. These paametes ae most effective in inceasing the ate of coect detection. Afte apply theshold value if the numbe skin pixels ae compaable with the lip pixels o the numbe of lip pixels is vey low, we assume that ou algoithm is failed in sepaating lip pixels fom skin pixels. In TABLE 2 we compae ou esults in lip segmentation with the esults of [2] and [5]. In Fig. 6 we pesent some images with extacted lip egion. These images indicate the pefomance of ou algoithm. Table 1. Results of ou lip aea detection and lip segmentation. Expession Accuacy (%) Lip aea detection Table 2. Compaison between the esults of ou algoithm and othe methods on CVL database. Accuacy (%) Expession Ou algoithm Method of [2] Method of [5] Seious Smile (showing no teeth) Smile (showing teeth) Total CONCLUSION Lip segmentation Seious Smile (showing no teeth) Smile (showing teeth) Total In this pape we poposed a novel algoithm fo lip aea detection and lip segmentation in colo face images based on local infomation. Local infomation in ou algoithm is standad deviation of pixels of diffeent pats in lowe half pat of face image. To educe the complexity and achieve bette esults, we popose to emove the uppe half of the face image. Fo detection of lip aea, we used the enhanced vesion of Lip-Map that poposed by Hsu et al. We multiplied this Lip-Map by satuation component. Afte dividing lowe half image into some equal pats, in ode to finding lip aea, we calculate standad deviation of pixels in each aea. Then, fo sepaating lip pixels fom skin pixels, we obtain optimum theshold value by Otsu s method. Ou expeimental esults show that this algoithm can find lip aea pecisely. The ate of lip segmentation fom skin is also bette than othe method. Fig. 5 Binay image of the lip aea; lip pixels in the oiginal image sepaated fom the skin pixels. 15
5 Kalbkhani and Chehel Amiani Fig. 6 Some images fom the CVL face database with the diffeent expessions and segmented lip pixels. REFERENCES [1] J. Shin, B. Jun, D. Kim, Robust two-stage lip tacke, IEEE Int. Symposium on Signal Pocessing and Infomation Technology (ISSPIT), pp , Dec [2] J. A. Nasii, H. S. Yazdi, M. A. Moulavi, M. Rouhani, A. E. Shagh, A PSO tuning appoach fo lip detection on colo images, Second UKSIM Euopean Symposium on Compute Modeling and Simulation, pp , Sept [3] E. Skodas and N. Fakotakis, An unconstained method fo lip detection in colo images, IEEE Int. Confeence on Acoustics, Speech and Signal Pocessing (ICASSP), pp , May [4] E. Gomez, C. Tavieso, J. Biceno, M. Fee, Biometic identification system by lip shape, Poceedings of the 36th Annual Intenational Canahan Confeence on Secuity Technology., pp , [5] R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, Face detection in colo images, IEEE Tansactions on Patten Analysis and Machine Intelligence, vol. 24, no. 5, pp , May [6] R. C. Gonzalez, and R. E. Woods, Digital Image Pocessing, Pentice Hall, 3d Edition, [7] Compute Vision laboatoy, Faculty of Compute and Infomation Science, Univesity of Ljubljana, Slovenia, 16
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