Chin Contour Extraction Based on an Auto-Initialized Shape-Enhanced Snake

Transcription

1 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October Chin Contour Extraction Based on an Auto-Initialized Shape-Enhanced Snake Kuo-Yu Chiu, Shih-Che Chien, and Sheng-Fuu Lin Department o Electrical Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, ROC Summary In this paper, an improved method to extract the chin contour under dierent ace poses is proposed. Several previous approaches have used active contour models(acm) or snakes in ront-view acial images. However, successul chin contour extraction or a wide range o ace poses has seldom been mentioned. An algorithm based on an improved snake or chin contour extraction is presented here and it is able to be operated successully with wide range o dierent ace poses. Since lots o algorithms have been proposed or extracting ace region and important acial eatures like eyes and lips, the algorithm in this paper assumes that the ace region and locations o these two eatures are known as prior knowledge. At the irst stage, the ace is normalized and ace pose is roughly estimated according to the geometric relationship between eyes and lips. Then, the snake or chin contour is initialized with two parabolas according to dierent ace poses. The shape-enhanced snake model is employed to it the chin contour at last. Experimental results show that the proposed algorithm can extract the chin contour under dierent ace poses with good accuracy. Key words: Active contour model, chin contour extraction, acial eature, ace pose 1. Introduction Chin contour extraction, or itting the chin, is a major requirement or ace modeling, ace pose estimation, ace replacement, and ace recognition. In act, chin contour is an important eature to distinguish the ace region rom the neck region. Besides, the shape o a chin also oers some inormation or ace recognition and ace pose estimation. While the shape o a chin is an important eature or a human, the chin contour is rarely used as a eature. One reason is that an estimated chin contour may deviate rom the true chin contour due to the weak boundary between ace region and neck region. With noise or complicated background, the estimated chin contour would easily be led into alse result. Another reason is that the chin contour varies greatly while the ace pose is dierent. When the ace is proile, turn 90 degrees let or right, the chin contour is totally dierent rom the rontal pose. Hence, chin contour extraction is one o the most diicult tasks o acial eature extraction. The shadows, variations o the skin color, clothing, hair, beards, and noisy background may easily reduce the accuracy o chin contour extraction. In order to overcome these problems, the prior inormation such like skin color region and acial eatures, such as eyes and lips, are important and necessary. Using these inormation, the typical shape o chin contour can be modeled and the possible locations o chin contour can be estimated. Since acial eatures extraction is an important and primary step or systems such as interaction systems and identiication systems, lots o algorithms and methods [1]-[5] have been proposed or extracting the acial eatures, which include skin color region, eyes, and lips region. Hence, we do not ocus on how to extract and localize these acial eatures in this paper. There are also some chin contour estimation or itting the chin algorithms. For example, the authors in [6] use color-based segmentation along with morphological operation to approximate the chin contour. The resulting set o discontinued segments and ragmented curves are processed, thinned, classiied, and inally the chin contour is reconstructed. In [7], the chin contour is estimated by a deormable template consisting o our parabolas. Costunctions are established and minimized to ind the best it o the template to the chin and cheek. A more accurate algorithm based on three models is proposed in [8]. Three perect models, circular, triangular, and trapezoidal models, are used here along with piecewise linear model or curve itting. The experiments show high accuracy or extraction and classiication on a large data set. Another algorithm based on two curves with polynomials o ive degrees are employed in [9][10]. By using modiied Canny edge detector, some alse edges with unit gradient directions are excluded and the results o itting are improved. There are other approaches based on active contour model [11]-[16]. As the snake model is an energy-minimizing spline inluenced by image eatures and external orces which pull the spline toward real edges, the results using a snake model or itting would be much Manuscript received October 5, 010 Manuscript revised October 0, 010

2 4 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October 010 closer to the real chin contour. However, when the chin contour appears loosely marked due to complicated background or proile ace pose, the reliability o snake model is greatly reduced. Furthermore, the perormance is aected seriously by the initialization and external orce o snake model. The algorithm proposed in [17] shows good accuracy when dealing with dierent ace poses. By training 10 lexible ace models based on dierent ace poses, the initialization problem is hence solved. In this paper, an algorithm based on snake or chin contour extraction which separates the chin region rom the neck region is presented. This algorithm is applicable to both rontal and proile ace images based on the inormation that the skin color region and the locations o eyes and lips are known. There are three main stages to accomplish the chin contour extraction. In the irst stage, the ace is irst normalized so that the roll angle is rotated to be zero. The line across two tips o the lips with proile ace pose or the line across two eyes with rontal ace pose is rotated to be parallel to the x-axis. The purpose o normalizing ace image is to acilitate locating the possible chin contour region. Then, the normalized ace image is roughly classiied into 7 classes, according to the ace turning angle and ace looking up, down, or straight as the second stage. The geometric relationship between eyes and lips is used as the input eatures or classiication. The snake model is dierently initialized according to these two classes in this stage. Finally, the shape-enhanced snake model is employed in the last stage to it the chin curve. The remainder o this paper is organized as ollows. In Section, the steps o ace image pre-processing including ace image normalization and ace pose classiication are given. Then, the proposed auto-initialized shape-enhanced snake algorithm or itting the chin contour is introduced in Section 3. Some experimental results and discussions are shown in Section 4. Finally, the conclusions are given in Section 5. skin color region and locations o eyes and lips, represented by their mass centers, are supposed to be known as prior inormation to utilize the estimation. The chin curve, as it can be seen, transorms greatly with dierent ace poses. When turning let or right, or looking up or down, these changes will lead to dierent chin curves as shown in Fig.1. A ixed model used as initialization o snake is not appropriate to describe the variety o chin contours. Since the snake is so sensitive to the initialized curve, dierent initial snakes should be built by considering dierent ace poses, instead o using one chin contour model. In this paper, ace poses are roughly divided into two classes, namely rontal pose and proile pose. These two classes are distinguished by the number o eyes. When two eyes are ound in a ace image, the ace pose is deined as rontal, while one eye represents proile ace pose. Since there may be two eyes or one eye in a ace image and the ace pose may be rontal or proile, these conditions are discussed and processed under dierent criteria. In this section, the ace image is irst normalized by setting the roll angle to be zero. Later, the ace pose can be roughly estimated and classiied according to the geometrical and statistical inormation between prior acial eatures. Then the estimated chin curve can be initialized close enough to the real one.. Face Image Pre-processing In this section, the ace image pre-processing steps including ace image normalization and ace pose classiication will be introduced. Active contour model or snake is an energy minimizing curve that deorms to it image eatures. However, there are some limitations, such like the model usually incorporate edge inormation, ignoring other image characteristics such as texture and color. Besides, the snake must be initialized close enough to the eature o interest to avoid being distracted by noise and clutter. By using the geometrical and statistical inormation between acial eatures, the chin curve can be roughly initialized with two parabolas. In this paper, the Fig.1 Chin contour varies greatly with the change o ace poses. When turning let or right, or looking up or down, these changes will lead to dierent chin contours..1 Face image normalization The purpose o ace image normalization is to rotate the ace image so that the ace is centrally located with zero roll angle. Most o the lines crossing important ace eatures, such like eyes, lips, or nose, are parallel to the x-axis or y-axis ater normalization, as shown in Fig.. The roll angle in this paper is determined in two ways

3 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October according to the number o eyes. With the prior inormation that the locations o eyes and lips are known, the roll angle θ is deined as the included angle between x-axis and the extended line across two eyes when two eyes are ound, y 1 r eye y l eye θ = tan ( ), (1) xr eye yl eye where y r-eye and x r-eye are the y-coordinate and x-coordinate o the right eye respectively, while y l-eye and x l-eye are the y-coordinate and x-coordinate o the let eye respectively. When there is only one eye in the ace image, which means the proile ace pose, the roll angel is deined as the included angle between x-axis and the extended line across the right and let tips o the lips, y 1 r lip y l lip θ = tan ( ), () xr lip yl lip where y r-lip and x r-lip are the y-coordinate and x-coordinate o the right tip o lips, while y l-lip and x l-lip are the y-coordinate and x-coordinate o the let tip o lips. Ater the roll angle is retrieved, the ace can be normalized to be horizontal by executing rigid transormation. The rigid transormation matrix M rig is deined by cosθ sinθ 0 M rig = sinθ cosθ 0. (3) Bilinear interpolation using the our nearest neighbors o a point is adopted here to modiy the rotated image. Let the point (x', y') denote the coordinate o interpolated pixel and v(x', y') denote the RGB value assigned to it. For bilinear interpolation, the assigned RGB value is given by vx ( ', y ') = ax ' + bv ' + cx ' y ' + d, (4) where our coeicients are determined rom the our equations in our unknowns that can be written using the our nearest neighbors o point (x', y')[19]. Fig. Original image. Rotated image with zero roll angle ater normalization.. Face pose estimation Ater the ace image is normalized, the ace pose owing to turning let or right and looking up or down is estimated. The ace pose can be divided into two major classes, namely rontal pose or proile pose, according to the number o eyes ound in a ace image. In an input ace image, ace pose is deined to be rontal i two eyes are ound, while proile ace pose means that only one eye is ound, as shown in Fig.3. Since the chin contour varies greatly when the ace pose is dierent, ace poses are urther discussed and classiied in this section. With the prior inormation o skin color region and locations o eyes and lips, 15 sub-classes in rontal pose and 1 sub-classes in proile pose are classiied by using a 3-layer neural network. Three parameters which derive rom the geometrical relationship o acial eatures are used as the input o the neural network. These parameters will be introduced as ollows. Fig.3 Face pose is deined to be rontal i two eyes are ound. Face pose is deined to be proile i only one eye is ound. Since the resolution and size may be dierent rom each other, by comparing with the ace image in data base, a scale actor, S, is deined here to represent the relative size o ace image or ace region. When the ace pose is rontal, or two eyes are ound, the scale actor S is deined by S = ( De e + Dl ee = EL ER + TP L c, (5) where D e-e is the distance between two eyes, E L and E R, and D l-ee is the vertical distance rom the centroid o the lips, L c, to the point, T p, which is the intersection o the line segment o two eyes and the vertical line crossing the centroid o the lips. These eature points are illustrated in Fig.4. From the statistical experiment results, the value o these two parameters, D e-e and D l-ee, are approximately proportional to the ace size and nearly equal to each other when the ace is rontal without turning let or right and looking up or down, while D e-e varies with the turning angle and D l-ee varies when looking up or down. Hence, the scale actor S is deined by using these two parameters. Though the skin color region is known as prior inormation, the size o skin color region is not used here

4 44 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October 010 to represent the size o ace image since skin color region extracted by skin color may contain neck region. Hence, the size o skin color region is not a valid parameter or ace size. When the ace pose is proile, or one eye is ound, S is deined with lesser acial inormation as S = Dl e = Eo L c, (6) where D l-e is the distance between the only eye, E o, and the centroid o lips, L c. Ater S is determined, we use some parameters as input eatures or ace pose classiication. From our experiments, three more robust and simpler parameters are used to describe the ace pose. With rontal ace pose, these eatures are deined as De e p1 = ( ), Dl ee (7) DTE r DTE l TP ER TP EL p = ( ) =, S S Fc FFc p3 = ( ), S where D TE-r is the distance between T p to right eye E R and D TE-l is the distance between T p to let eye E L. When the ace is turning let (rom the viewpoint o an observer), D TE-r would be larger than D TE-l and the result o p will be positive. The dierence value between D TE-r and D TE-l is divided by the actor S to represent a relative turning degree. In last equation, the mass centers o ace region and acial eatures are utilized. We use F c to represent the x-coordinate o skin color region centroid. The x-coordinate acial eature centroid determined by the mass center o E L, E R, and L c is deined as acial eature centroid FF c. These two eatures are illustrated in Fig.4 and Fig.4 respectively. Since only x-coordinate is concerned in this equation, the skin color region containing neck region or not does not matter. The skin color region with neck region changes the centroid o skin color region center in y-axis much more than that in x-axis. With the ace turning let, the acial eature centroid moves leter than the skin color region centroid does. A positive p 3 represents that the ace is turning let and the magnitude show the turning degree. Fig.4 Facial eatures and the centroid o acial eatures o rontal ace pose. Face centroid is deined as the centroid o skin color region. When dealing with proile ace poses, we irst determine whether the head is turning let or right by comparing the x -coordinates o skin color region centroid and acial eature centroid, which is derived rom the centroid o lips only. Then, the chin tip, C T, is extracted by C = max xy, (8) T ( xy, ) F where is the set o points o skin color region. By setting the y-coordinate o the highest skin-color point and the x -coordinate o the most right skin-color point when turning let or the most let skin-color point when turning right as origin, the variable x and y are the relative x-coordinate and y-coordinate o points o skin color region. Connecting chin tip and lips tip as L 1 and connecting chin tip and the eye as L, we deine another three eatures or classiication. These eatures are deined by DE o L1 p1 = ( ), S (9) p = θl, 1 p3 = θl, where D Eo-L1 is the distance rom the eye to L 1. When the ace pose is more proile, the distance rom the eye to L 1 will be shorter. The parameter θ L1 is the included angle between L 1 and x-axis, whileθ L is the included angle between L and x-axis. These two eatures vary with the ace looking up or down and turning right or let. All the eatures are shown in Fig. 5. Fig.5 Facial eatures and some eature points or lines o proile ace pose. Now, or each ace image, we have three eatures derived rom the geometrical relationship and treat them as the input o neural network. The outputs o neural network are the types o ace pose. When the ace pose is rontal, there are 15 classes composed o 5 types o ace turning with looking up, down, and straight. The 5 types are -50 ~ -30, -30 ~ -10, -10 ~ 10, 10 ~ 30, and 30 ~50 respectively. The positive degree represents that the ace is turning right rom the view o an observer, while negative degree means turning let. The ace pose is deined as looking straight i the tilt angle owing to looking up or down is between -0 and 0. Hence, the tilt angle

5 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October greater than 0 means looking up and less than -0 represents looking down. When the ace pose is proile, there are 4 types o ace turning with looking up, down, and straight, totally 1 classes. The 4 types o ace turning are -90 ~ -70, -70 ~ -50, 50 ~ 70, and 70 ~ 90. The deinition o looking up, down and straight is the same as beore. Figure Fig.6 shows an example o all the 7 dierent ace poses. Each ace in Fig.6 belongs to one o these classes. Ater training the neural network with test data rom both real ace images and virtual ace images by using the eatures deined beore, the ace pose o an input ace image can be roughly classiied. Fig.6 The representative ace images o 7 dierent ace pose classes. 3. Chin Contour Extraction In this section, chin contour extraction based on auto-initialized shape-enhanced snake is introduced. The snake model is an energy-minimizing active contour composed o image eatures, internal constraints and external orces. Snakes lock on to local minima in the potential energy generated by processing an image. However, there are limitations such like that the models must be initialized close to the eature o interest to avoid being distracted by noise and clutter. In chin contour extraction problem, chin contour is weak and indistinct, and it varies greatly when the ace pose is dierent. Besides, there is some noise nearby such like beards or collars. With the inormation o ace pose obtained beore, the snake can be initialized appropriately to the real chin contour to solve these problems. A chin contour database o various ace pose is irst built. We use two parabolas to describe the chin contour based on the chin tip. Let chin and right chin are estimated by a parabola respectively. These parabolas are estimated by using least square error (LSE) method with the chin contour points extracted by man. Then, the initial control points can be set with this inormation. At last, an iterative shape-enhanced snake is adopted here to it the chin contour, which ocuses on the initial shape and the distance between initial position and inal position. These will be introduced in the ollowing sub-sections. 3.1 Snake auto-initialization Generally, the initial snake can be set arbitrarily in any shape, such as circle or square. However, i the shape o snake is similar to the target and the position is near to the target, a better and aster result will be obtained. The purpose o snake auto-initialization is to it the chin contour as similar and near as possible with the ace pose inormation. Beore initializing the snake, a chin contour database based on ace pose and statistical results is built. The initial snake control points are the linear transormation o the chin contour stored in database. We determine one typical chin contour as the average ace pose or each class o ace pose, and there are thereore 7 typical chin contours in the database. For example, the chin contour with 60 turning right looking straight ace pose will represent all the class o 50 ~ 70 turning right looking straight ace pose, while 0 looking up and -0 looking down will represent the class o looking up and looking down. Each typical chin contour is extracted by man. We deine the matching center, M c, as the center o lips when the ace pose is rontal or as the interior tip o lips when the ace pose is proile. Two parabolas, L and R, separated by the x-coordinate o M c with rontal ace pose and C T with proile ace pose are constructed to it the typical chin contour, as shown in Fig. 7. Using least square error (LSE) method, the parabola on the let, L, can be derived rom the ollowing equation x 1 T T T y = L( x) = XLX L XLY L x, (10) 1 where x1 x1 1 y1 1 y x x, XL = YL =, (11) M M M M xc xc 1 yc and (x k, y k ), k = 1,...,c, is the coordinate o typical chin contour on the let extracted by man. The right parabola can also be derived in a similar way. These two parabolas o each typical chin contour are built in the database or snake initialization.

6 46 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October 010 Fig.7 The matching center and the reconstructed chin contour with two parabolas in rontal ace pose, and proile ace pose. However, since the scale and the position o input ace image may not be the same as those in database, scaling and shiting is needed. When the input image ace is in high resolution, more control points are needed as shown in Fig.8, while ew control points are enough to represent the chin contour in ace images with poor resolution. To maintain accuracy o chin estimation and simplicity o calculation, the number o control points N cp is adaptive to the scale o input ace image and is determined by ' S Ncp = 7+, (1) 40 with rontal ace pose, or is determined by ' S Ncp = 9+, (13) 30 with proile ace pose where S' is the scale actor o input ace image. These equations and parameters are derived rom statistical experiment results. Because the proile ace pose is more complicated with more curves and details, more control points are also needed to express the chin contour, as shown in Fig.8. Fig.8 The ace image with higher resolution need more control points to represent the chin contour. There are more details on the chin contour o proile ace pose, so more control points are also needed. The initial control points are linear transormation o some points o parabolas stored in database. Suppose '={s' 1, s',, s' Ncp,} is the set o the initialized snake control points containing N cp elements as shown in Fig. 9, each o the control point is auto-initialized by ' s ' ' sk = [ sk Mc] + M c, k = 1,,..., Ncp, (14) s where s' k, M', M' c, and S' are the coordinates o control points, matching center, and scale actor o input ace image, while s k, M, M c, and S are those o typical chin contour with the same class o ace pose. The control point s k o typical chin contour is deined by y 1 M y π c s k = ( xy, ) tan ( ) = ( k 1),( xy, ) x y, xm x N 1 (15) c cp k= 1,,..., N, cp where (y Mc, x Mc) is the coordinate o M c, as shown in Fig.9. Fig.9 The control points o typical chin contour. The auto -initialized control points derived rom the typical chin contour. 3. Shape-enhanced snake The basic snake model is an energy-minimizing spline. A snake is represented as a parametric curve v(k)=(x(k), y(k)), where the arc length, k=1,,, N cp, is an index. The energy unction o snake is deined as[18] Esnake = ( αeinternal ( v( k)) + βeimage ( v( k)) + γeexternal ( v ( k))) ds,(16) where E internal is the internal energy o the contour composed o tension and stiness, E image represents the energy o the image, E external is the energy o the external constraint, α, β, and γ are weighting parameters. The internal energy incorporates regularizing constraints that give the model tension and stiness, and is deined as Einternal = α1etension ( v( k)) + αestiness ( v( k)), (17) = α1v'( k) + αv''( k) where v'(k) and v''(k) controlled by weighting parameters α 1 and α are irst- and second-order terms that produce tension and stiness respectively. The image energy drives the model towards salient eatures, which is usually generated by processing the image to enhance edges, terminations, or eatures. The image energy is deined as E = I( x, y), (18) image

7 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October where I(x,y) is the gradient image. The external energy represents external constraints imposed by high-level sources such as human operators. Here, we consider two constraints as snake external energy. First, since the snake is appropriately initialized, the real chin contour would be close to the initialized snake. When the chin contour is not salient with proile ace pose or when there is lots o noise nearby, the auto-initialized snake should be used as important inormation. The distance o each control point between the inal snake and the initialized snake should be minimized. With this constraint, the chance that the snake alls into another contour such as the neck o clothes will be reduced. Second, when there are errors in eature extraction, transormations o control points including shit or size change are predictable. Under this condition, all the control points will trend to the same direction. With above two considerations, the modiied external energy o shape -enhanced snake in this paper is deined as Eexternal = γ1 () i k + γσ k, (19) where γ 1 and γ are the weighting parameters, i(s) is the searching index whose sign is the searching direction and magnitude is the distance between snake control point o initialization and current iteration, and σ k is the standard deviation o i(k). Since the chin contour is properly initialized, the result o estimation should be close to the initialized chin contour. The distance between control points o the initial snake and the inal result is minimized. Hence, the irst term strengthen the shape o initialized snake and a smaller i(k) indicates the snake ater iterations is close to the initial one. The second term ocuses on the consistency. A smaller σ i would be derived i most o the searching indexes including direction and magnitude are similar. When there are errors between acial extraction or eature matching, the initialized chin contour is a transormation o the inal results. A minimized σ k implies that the degree o shit and translation is similar. Since the real chin contour is near the initialized snake, the possible searching range can be conined with the inormation o errors and scale. The errors include the estimated error between the typical chin contour model and all the chin contours in the same class, and the matching error brought rom the unprecise location o matching centers. These errors will be larger with high ace image resolution. Hence, ater the snake is initialized, the possible searching range o one control point can be determined by v( k) = v( k) + i( k) n v ( k), Imax ( k) i( k) Imax ( k), (0) where v ( k) and n v ( k) are the corresponding initial control point and unit normal vector respectively. The unit normal vector means that the possible searching region ollows the direction o normal vector. The maximum searching index I max determined by the inormation o errors and scale is deined as ' S Imax ( k) = ε ( k) Δ, (1) m S where ε(k) is the maximum error or control point v ( k) between the typical chin contour model and all the chin contour in the same class, while Δ m is a constant error which includes the matching error and the localization error o acial eatures. Ater the searching region is determined, the auto-initialized shape-enhanced snake will lead to estimated chin contour by minimizing the energy iteratively. The result chin contour is obtained when the energy is minimized or several iterations are run, such as 100 iterations. We use cubic spline to express the inal results, as shown in Fig.10. A cubic spline is a spline constructed by piecewise third-order polynomials which pass through a set o control points. By deining the cubic spline as 3 1 v( k ) = a3sk + ask + as 1 k + a 0, () the parameters, a 0, a 1, a, and a 3 can be derived by dierentiating the cubic spline equation. These parameters are expressed as a3 = v( k) v( k+ 1) + v'( k) + v'( k+ 1), a = 3 v( k) + 3 v( k+ 1) v'( k) v'( k+ 1), (3) a1 = v'( k), a0 = v( k), k = 1,,..., Ncp 1, and the spline unction is obtained by a3 a 3 v( t) = ( k) ( k) ( k) 1 v v v a1 a0 1 1 v( k) ( k 1) (4) + k k k 1 v = s s s, v '( k) v'( k + 1) t k k+ 1, [ ] Fig.10 The cubic spline is a continuous unction constructed by piecewise third-order polynomials which pass through all the control points.

8 48 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October Experiment Results and Discussions Our experiment results are divided into two parts. One is the ace pose estimation, and the other one is chin contour estimation. For the results o ace pose estimation, we use a database containing 70 real ace images rom 10 people and 810 virtual ace images. Since there are total 7 classes o ace pose in this paper, 10 real ace images and 30 virtual ace images are used as training data or each class o ace pose. The real images oer the variety o ace shape, such as the atter ace, thinner ace, or ace with double chin. The virtual ace images, on the other hand, give various ace poses since the degree o turning and looking up or down can be easily set by adjusting the parameters. All the 1080 ace images are used as input training data or a 3-layer neural network, and the output are the 7 classes o ace pose. Each class o ace pose is described by a typical chin contour model which consists o two parabolas. Since the ace pose o real ace image is unable to be correctly classiied, we use another 540 virtual ace images o random ace poses or testing, which means 0 test images are used or each class. The results o ace pose estimation are shown in Fig.11. From the experimental results, we can see that the ace pose estimation algorithm proposed in this paper gives an acceptable average accuracy as about 91%. Besides, analyzing the wrong results, all the misclassiied ace poses are classiied into the adjacent class o the correct class. Since the estimated ace poses are used as snake initialization, the estimation results are acceptable and useul or the urther chin contour estimation. inormation, the searching range or chin contour could be set reasonably. Some cases are concerned in our experiments, which include aces with dierent resolution, sizes, and ace poses. In this paper, we deined the scaling actor S which oers the inormation o relative ace size and image resolution. As shown in Fig.1, the resolution o input ace image is much higher than the ace image in database. By using the inormation o relative scaling actor, the initialized snake can be resized to it the real chin contour. When the ace is wider or narrower and longer in shape, the chin contour can be extracted more precisely under the limited searching range as shown in Fig.13. While dealing with dierent ace poses, they are roughly classiied into 7 classes and each o them has a typical initialized chin contour composed o two parabolas. In this way, this method proposed in this paper greatly improves the snake initialization drawback when coping with chin contour extraction. Besides, chin contour varies severely under dierent ace pose and extraction or chin contour o proile ace poses is seldom mentioned in previous papers because o the weak boundary. The ace pose based auto-initialization shape-enhanced snake provides a way to approach the chin contour with proile ace poses as illustrated in Fig.14. Fig.11 The accuracy o ace pose classiication. The average accuracy o ace pose classiication is 94.07%. Ater the ace pose is classiied, the snake model can be initialized. The maximum errors between the initialized chin contour and the real chin contour are derived rom the testing 540 virtual ace images. We do not use the real ace image because the chin contour o real ace image can be extracted by man only. The task is time consuming and the chin contour extracted by man is a little dierent time by time. Contrarily, the chin contour o virtual image could be extracted easily by program and the points o chin contour can be determined previously. Hence, the maximum errors are derived rom the virtual ace image. With this Fig.1 The typical chin contour model stored in database. The initialized control points. (c) The control points ater several iterations. (d) The reconstructed chin contour.

9 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October Fig.13 The typical chin contour model stored in database is thinner than the input ace. The chin contour can be properly estimated under limited searching range. Fig.15 The input ace image is a atter ace with double chin, proile ace pose, looking down, and complex background. The reconstructed chin contour. Fig.14 The initialized control points o chin contour. The autoinitialized shape-enhanced snake oers an approach to the proile ace pose. Also, we consider some special cases. In Fig.15, a atter ace with double chin, proile ace pose, looking down, and complex background is tested. The wrinkle ormed because o ace pose or atty ace shape leads into a local minimum. The ace image is more complicated and with more details on it. From the experimental results, the proposed method in this paper oers an acceptable approach to the test input image. Another contrary example is given in Fig.16. The input image is a ace with long thin chin and the initial chin contour is hence ar away rom the real chin contour. Ater several iterations, the proposed snake successully converges to the real chin contour. When there are some objects or noise near the chin contour, the proposed method also has good perormance. For example, consider the ace image with a cell phone near the ace as shown in Fig.17, the integrity and the smoothness o the estimated chin contour is maintained. Except the internal constraint o stiness and tension, this is not only because that the auto-initialized shape-enhanced snake oers a prototype o chin contour but because that the normal vector is used to narrow the searching region to a line segment instead o an area, which reduces the opportunity o alling into alse minimum. Hence, a proper chin contour is derived by the proposed method, as shown in Fig.17. Fig.16 Face image with a long and thin chin. Dots in green are the initial control points, while dots in red are the estimated chin contour. An estimated chin contour is established when the chin is thinner and longer than the one in data base. Fig.17 Face image with a cell phone near the ace The chin contour is estimated with the inormation o initialized snake. 5. Conclusions In this paper, a chin contour estimation algorithm is proposed, which is based on an auto-initialized shape-enhanced snake. By roughly estimated the ace poses, the control points o snake can be appropriately initialized according to the 7 dierent ace poses. The drawback o snake initialization is thereore improved. When dealing with the proile ace poses, the autoinitialized snake along with the enhanced snake shape oers an approach to the proile ace poses, even the boundary o proile ace poses are very poor. The

10 50 IJCSNS International Journal o Computer Science and Network Security, VOL.10 No.10, October 010 experimental results show that the proposed method can estimate the chin contour with reliability and robustness under various ace poses and several situations. Reerences [1] U. Qayyum and M. Y. Javed, Real time notch based ace detection, tracking and acial eature localization, in Int. Con. on Emerging Technologies, 006. ICET'06, Nov. 006, pp [] R. Belaroussi and M. Milgram, Face tracking and acial eature detection with a webcam, in 3rd European Con. on Visual Media Production, 006. CVMP 006, Nov. 006, pp [3] R. Hepers, G. Verghese, K. Derpanis, R. McCready, J. Maclean, A. Levin, D. Topalovic, L. Wood, A. Jepson, and J. Tsotsos, Detection and tracking o aces in real environments, in Proc. Int. Workshop on Recognition, Analysis, and Tracking o Faces and Gestures in Real-Time Systems, Sep. 1999, pp [4] M. H. Yang and N. Ahuja, Detecting human aces in color images, in Proc. Int. Con. on Image Processing, vol. 1, Oct. 1998, pp [5] L. V. Praseeda, S. Kumar, and D. S. Vidyadharan, Face detection and localization o acial eatures in still and video images, in First Int. Con. on Emerging Trends in Engineering and Technology, ICETET '08, Jul. 008, pp [6] S. Boursas and H. Yan, Chin extraction in colour rontal-view ace images,' in Digital Image Computing Techniques and Applications, Melbourne, Australia, Jan. 00, pp [7] M. Kampmann, Estimation o the chin and cheek contours or precise ace model adaptation, in Proc. ICIP 97}, Oct. 1997, pp [8] J. Y. Wang, G. D. Su, and X. G. Lin, The research o chin contour in ronto-parallel images, in Proc. Second Int. Con. on Machine Learning and Cybernetics}, vol. 5, Mar. 003, pp [9] Q. R. Chen, W. K. Cham, and K. K. Lee, Chin contour estimation or ace recognition, in IEEE Int. Con. on Acoustics, Speech, and Signal Processing (ICASSP03)}, vol. 6, Apr. 003, pp [10] Q. R. Chen, W. K. Cham, and K. K. 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Lin, Improved chin itting algorithm based on an adaptive snake, in Proc. Int. Con. on Intelligent Computing, ICIC 005, Heei, China, Aug. 005, pp [17] X. L. Ge, J. Yang, Z. L. Zheng, and F. Li, Multi-view based ace chin contour xtraction, Engineering Applications o Artiicial Intelligence, vol. 19, 006, pp [18] M. Kass, A. Witkin, and D. Terzopoulos, Snake: active contour models, Int. J. Comouter Vision, vol. 1(4), 1987, pp [19] R. C. Gonzalez and R. E. Wood, Digital Image Processing, nd ed. Prentice Hall, 001. Kuo-Yu Chiu was born in Hsinchu, R.O.C., in He received the B.E. degree in electrical and control engineering rom National Chiao Tung University, Hsinchu, Taiwan, R.O.C, in 003. He is currently pursuing the Ph. D. degree in the Department o Electrical and Control Engineering, the National Chiao Tung University, Hsinchu, Taiwan. His current research interests include image processing, ace recognition, ace pose estimation, ace replacement, intelligent transportation system, and machine learning. Shih-Che Chien was born in Chiayi, R.O.C., in He received the B.E. degree in electronic engineering rom the Nation Chung Cheng University, in 00. He is currently pursuing the M.E. and Ph.D. degree in the Department o Electrical and Control Engineering, the National Chiao Tung University, Hsinchu, Taiwan. His current research interests include image processing, image recognition, uzzy theory, 3D image processing, intelligent transportation system, and animation. Sheng-Fuu Lin (S 84 M 88) was born in Tainan, R.O.C., in He received the B.S. and M.S. degrees in mathematics rom National Taiwan Normal University in 1976 and 1979, respectively, the M.S. degree in computer science rom the University o Maryland, College Park, in 1985, and the Ph.D. degree in electrical engineering rom the University o Illinois, Champaign, in Since 1988, he has been on the aculty o the Department o Electrical and Control Engineering at National Chiao Tung University, Hsinchu, Taiwan, where he is currently a Proessor. His research interests include image processing, image recognition, uzzy theory, automatic target recognition, and scheduling.