Eyes Detection in Facial Images using Circular Hough Transform

W. M. K Wan Mohd Khairosfaizal and A. J. NorainiFaculty of Electrical Engineering,

Universiti Teknologi Mara,40450 Shah Alam, Selangor, Malaysia

Abstract-This paper presents an eye detection approach using Circular Hough transform. Assuming the face region has already been detected by any of the accurate existing face detection methods, the search of eye pair relies primarily on the circular shape of the eye in two-dimensional image. The eyes detection process includes preprocessing that filtered and cropped the face images and Circular Hough Transform is used to detect the circular shape of the eye and to mark the eye pair on the image precisely. This eyes detection method was tested on Face DB database developed by Park Lab, University of Illinois at Urbana Champaign USA. Most of the faces are frontal with open eyes and some are tilted upwards or downwards. The detection accuracy of the proposed method is about 86%.

Keyword- Accumulation array, Gradient magnitude, Gradient thresholding, Circular Hough Transform

I. INTRODUCTION

Human eyes play an important role in face recognition and facial expression analysis. In fact, the eyes can be considered salient and relatively stable feature on the face in comparison with other facial features. Eye detection is valuable in determining the orientation of the face and also the gaze direction. The position of other facial features can be estimated using the eye position [1]. In addition, the size, the location and the image-plane rotation of face in the image can be normalized by only the position of both eyes. This is also regarded as one of the most important biometrics characteristics for personal identification.

The existing work in eye position detection can be classified into two categories. First, the active infrared (IR) based approaches and second the image-based passive approaches. Eye detection based on active remote IR illumination is a simple yet effective approach [2]. But it relieson an active IR light source to produce the dark or bright pupil effects. In other words, this method can only be applied to the IR illuminated eye images. This method is not widely used, because in many real applications the face images are not IR illuminated.

The image-based passive methods can be classified into three categories. First, template based method [3-6], secondly is the appearance based method [7-9] and the third is feature based method [10-14]. In the template based method, a generic eye model, based on the eye shape, is designed first. Template matching is then used to search the image for the eyes. While this method can detect eyes accurately, it is normally time-

consuming. The appearance based method detects eyes based on their photometric appearance. This method usually needs to collect a large amount of training data, representing the eyes of different subjects, under different face orientations, and under different illumination conditions. These data are used to train a classifier such as a neural network or the support vector machine and detection is achieved via classification. Feature based methods explore the characteristics such as edge and intensity of iris, the color distributions of the sclera and the flesh of the eyes to identify some distinctive features around the eyes. Although this method is usually efficient, they lack of accuracy for the images which do not have high contrast. For example, these techniques may have mistaken eyebrows for eyes.

In this paper, the face detection is carried out on the identified face region without detecting the face region first as the main focus is to detect eye pair from the face image. A simple yet robust algorithm to locate the eye pair on grey intensity face images is proposed. Currently, there are lots of promising face detection methods [15-18] exist thus assumptions have been made in this work such that (1) a rough face region has been located, (2) the image consists of only one face and (3) eyes in face image can be seen. The image-based eye detection approaches is used to locate the eyes by exploiting eyes differences in appearance and shape from the rest of the face. The special characteristics of the eye such as dark pupil, white sclera, circular iris, eye corners, eye shapeand etcetera are utilized to distinguish the human eyes from other objects. The steps involve in the eye detection process are cropping the face images to the required face region, threshold on the gradient magnitude of the face images to get the linear indices in the images and since the iris is nearly circular, the Hough transform is used to detect the circular shape of the iris of the human eye based on the linear indices. The pupil of the eye is plotted as the circle center and the circular shape of the iris is located and drawn as the circle parameter with its specific radius from the circle center. The proposed method is expected to increase the efficiency of feature based methods.

II. METHODOLOGY

The block diagram of the proposed approach for the eye detection is shown in Figure 1.

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The process of detecting the eye pair in the face image starts with acquiring the grey scale face image from the face database. The image must be two dimensions with the rough face region consists of a face and eyes. The algorithm built can only be used under this situation. The output image is known as the raw image. Face detection will process first locate the rough face region. In the second stage, an efficient feature-based method is used to locate two rough regions of the eyes in the face, which is the objective of the study.

A. Preprocessing In order to obtain a proper segmentation of the image, pre-processing of the image is carried out. To compensate for illumination variations and to obtain more image details, a median filter is used to enhance the brightness and the contrast of the images [20]. It is also used to eliminate the noise from the raw image. A median filter is based upon moving a window over an image and computing the output pixel as the median value of the brightness within the input image. A useful variation on the theme of the median filter is the percentile filter. Here the centre pixel in the window is replaced not by the 50% (median) brightness value but rather by the p% brightness value where p% ranges from 0% (the minimum filter) to 100% (the maximum filter). Values other then (p=50) % do not, in general, correspond to smoothing filters. This step simultaneously normalizes the brightness across an image and increases contrast. As a result, the image is enhanced and corrected from noise. The face region from the filtered image is cropped out from the background. This is done to eliminate the unwanted region and also to facilitate the process of detecting the eyes. The output image from this stage is known as the filtered image.

B. Eye Pair Detection When the rough face region is detected, the eye pair detection is sequentially applied to locate the rough regions of both eyes. Figure 2 shows the process of the proposed method.

C. Validation of Image Parameter. This step is to validate the filtered image parameters in order to ensure that the subsequent algorithms used can be applied. The parameters that need to be considered are as follow,

i. Dimension (2-D)ii. Size (minimum 32X32)

iii. Type ( greyscale image)

D. Building the Accumulation Array To build the accumulation array, the first step is to compute the gradient and the gradient magnitude of the roughface image region. It is the first derivative of two-dimensional image. The equations used are as follow:

i. Two dimensional first derivative; (1)

where; xh :denotes a horizontal derivative, yh :denotes a vertical derivative,

h :denotes the arbitrary angle derivative.

ii. Gradient, a[m,n], of an image:

yyxxy iahiahi

y

aix

x

aa

(2)where ix and iy are unit vectors in the horizontal and vertical direction, respectively.

iii. Gradient magnitude,

2y2x ahaha (3)

FACE IMAGE

FILTER(Median Filter)

CROP(Extacting the Face

Region)

Eye Pair Detection

Preprocessing

Fig. 1. Block Diagram of the Eye Detection Process

yx hsinhcosh

Validation of Parameter(Accumulator Building)

Area of Interest

Hough Transform to Detect Eye

(Circular Hough Transform)

Fig. 2. Block Diagram of the Eye Pair Detection Process

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Approximated by;

ahaha yx ~ (4)

The linear indices of the gradient magnitude are computedusing the equation as follows;

jijn1j

kik Xaxf (5)

where; ija :gradient magnitude,

jX : Symmetry square matrix,

ik xf : Linear indices of the gradient magnitude.

The accumulation array of the image consists of the gradient magnitude of the image and its linear indices as in equation (6).

Accumulator = (Gradient Magnitude, Linear Indices) (6)

E. Area of Interest From the segmentation process of the accumulator, the segmented accumulator is smoothened to get better segmented value using averaging filter. To obtain the area of interest, local maxima mapping on the image face region is generated by locating every local maximum on the segmented region. Local maximum filter is built by thresholding the local maxima mapping with the lower bound value. There are twosteps to be done separately. First, the segmented accumulator is threshold with the non-segmented accumulator and filtered by the local maximum filter. Next step is to label the generated local maximum mapping by eight connected component as in Figure 3 and then threshold by the gradient component. The threshold process is known here by gradient threshold and basically takes the adaptive threshold method which threshold value varies across the entire image. The equation of the threshold method is as in Equation (7). The output from the second step is known as mask.

Fig. 3. Label 8 Connected Component

cffTT , (7) where; T is the threshold

f is the whole image,

cf is 8 label image part.

The results from both steps are compared to select the area of interest in the face image. The comparison of both value and the reconstructed data image are as in Figures 4 and 5respectively:

Fig. 4. Comparison Graph of values from steps 1 and 2

Fig. 5. Reconstructed Image

The reconstructed image indicates which area in the image can be considered as the area of interest (location of eye pair). The clipping value as seen in the graph is threshold by the gradient magnitude values at the respective area. The respective values in the accumulator array are replaced by these new threshold values. Then the process of locating the local maxima on every threshold area is done to detect the eye area in the image. As the accumulator array of the reconstructed image can be converted into a function of f(x), then the local maximum is at xo for a>0 such that, for x (xo-a, xo +a) there is f(x)f(xo). Intuitively, it means that around xo the graph of f will be below f(xo). Each area of interest is compiled together as further process is done only among these components. Each local maxima candidate in every area of interest is compiled in a group by selecting minimum number of qualified pixel in each group of the interested area.

F. Circular Hough Transform to Detect Circle of the Eyes Hough transform is a technique which can be used to isolate features of a particular shape within an image. Because it requires the desired features to be specified in some parametric form, the classical Hough transform is most commonly used for the detection of regular curves such as lines, circles, ellipses, and etcetera [21]. The main advantage of the Hough transform technique is that it is tolerant to gaps in feature boundary descriptions and is relatively unaffected by image noise. To detect the eye which is circular in shape the so called Circular Hough Transform is used as in equation(8).

22020 ryyxx (8)

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where, 00 y,x is the coordinate of the circle centre, r is the radius of the circle.

The detection process starts with the local maxima in the group of the area of interest is assumed as the centre of the circle. If the linear indices among the minimum value of qualified pixel forming the circular shape, then that area is the eye region detected on the image. Every area of interest is tested with this process for it occurs as an element of the circle component which is the eye region identified in the image.

III. RESULT AND DISCUSSION

The test on using the proposed method was conducted on a well known Face DB database [22]. The Face DB database has been developed by the University of Illinois at Urbana-Champaign under their Productive Aging Laboratory. The laboratory is also known as the Park Lab, which was named after the founder of the laboratory, Dr Denise C. Park. In this database there are several types of face images either colouredimages (RGB) or intensity images (gray-scale).The image format also varies from Windows Bitmap (BMP) to Joint Photographic Expert Group (JPEG). The images selected are in two-dimension with size consists of 646 x 480 gray-scale,BMP format consist of 72 face images with a constraint background. The images consist of three ethnics which are the Asians, African-Americans and Caucasians. The categorizingsets a wider illumination based on their skin colour. Furthermore, each category has been subdivided into set of age ranging from 19 to 84 years old. Out of 72 face images, 50 images have well opened eyes while the rest have eyes partially opened. The developed software is written in Matlab codes and the results from the eye detection process are shown in figures 6 to 12.

Fig. 6. Original image

Fig. 7. Filtered and Cropped Face Image Region.

Generated map of local maxima

50 100 150 200

50

100

150

200

250

Fig. 8. Generated Map on Local Maxima

050

100150

200250

0

100

200

300

-40

-20

0

20

40

Accumulation array after local maximum filtering

Fig. 9. 3-D View of Accumulation Array after Local Maximum Filtering

Accumulation Array from Circular Hough Transform

50 100 150 200

50

100

150

200

250

300

350

Fig. 10. Accumulation Array from Circular Hough Transform.

050

100150

200250

0

100

200

300

400

0

200

400

600

800

3-D View of the Accumulation Array

Fig. 11. 3-D View of the Accumulation Array from Circular HoughTransform

Grayscale Image with Detected Eye Circle(center positions and radii marked)

50 100 150 200

50

100

150

200

250

300

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Fig. 12. The Face Image With the Eyes Pair Detected.

The original face is shown in Figure 6 and Figure 7 shows the original face that has been cropped to obtain the face region from the facial image and filtered using median filter. Other than reduced in noise the filtered image has also been enhanced in term of its brightness and contrast as to

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compensate for its illumination variations to obtain more image details. Figure 8 shows the generated local maxima mapping on the accumulation array for selecting the area of interest which are the possible area of the eyes in the image. Figure 9 shows the 3-D view of the accumulation array after local maximum filtering. Figures 10 and 11 are the results from the Circular Hough Transform to detect the circular shape of the eye pair in the image. Finally, Figure 12 shows the eye pair in the face image which was marked with a cross (+) at the centre of the circle. From Figure 11, the 3-D view of the accumulation array shows three points of the local maximum. Two points for the eye regions and one point onthe hair of the person. Since the Circular Hough Transform is used to detect the region of the eye pair, only circular shape is detected as in Figure 12. As the hair point is not circular in shape, the system did not detect this point as the eye region on the face image.

The evaluation on the performance of the proposed algorithm is carried out on 50 face images with opened eyes. Figure 13 (a) and (b) are some examples of the face images for which the proposed algorithm correctly detect the eye pairwhile Figure 14 (a) and (b) show some example of which the proposed algorithm failed to detect the eye pair. Since Circular Hough Transform detects circular shape, the algorithm detects another circular shape on the face image due to the circular shape of the nostril.

(a) (b)Fig.13. Detected eye pair

This happens because the face is tilted up and the nostrils are exposed that cause the algorithm to wrongly detects the nostrils as the eye region due to its circular shape Other factor could be due to illumination since the circular white spot on the noise of Figure 14 (b) was mistaken as the eye region.

(a) (b)Fig. 14. Wrong Detection of the eye pair

IV. CONCLUSION

The success rate of the eyes being detected is about 86% that equals to 43 eye pair detected from 50 face images. The filter used could not totally eliminate the effect of illumination variations for all the images tested which causes the false detection of the eyes. Perhaps using a better filter that able to

rectify this problem can increase the number of eye pair being detected.Other then that, the combination of other techniquescan be considered to eliminate the unwanted details on the face. However the Circular Hough Transform is a relevant algorithm to be considered in the eye detection process due to.

REFERENCES

[1] F. R. Brunelli and T. Poggio, Face recognition features versus templates , Pattern IEEE Transaction Anal.ysis Machine Intelligent15 (10) (1993) 10421052.

[2] Carlos Morimoto et al, Real-Time Detection of Eyes and Faces, http:www.cs.ucsb.edu/conferences/PUI/PUIworkshop98/papers/Morimoto.pdf, 1998.

[3] A.L. Yuille, P.W. Hallinan and D.S. Cohen, Feature extraction from faces using deformable templates , International. Journal,Computer. Vision 8 (2) (1992) 99111.

[4] X. Xie, R. Sudhakar and H. Zhuang, On improving eye feature extraction using deformable templates, Pattern Recognition. 27 (1994) 791799.

[5] K.M. Lam and H. Yan, Locating and extracting the eye in human face images, Pattern Recognition. 29 (1996) 771779.

[6] M.Nixon, Eye Spacing measurement for facial recognition, Proceeding of the Society of Photo-Optical Instrument Engineers, 1985.

[7] A. Pentland, B. Moghaddam and T. Starner, View-based and modular eigenspaces for face recognition, Proceeding. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR94), Seattle, WA, 1994

[8] W. min Huang and R. Mariani, Face detection and precise eyes location, Proc. Int. Conf. on Pattern Recognition (ICPR00), 2000.

[9] J. Huang, H. Wechsler, Eye detection using optimal wavelet packets and radial basis functions (rbfs), International Journal of Pattern Recognition. Artificial Intelligent 13 (7) (1999) 10091025.

[10] G.C. Feng and P.C. Yuen, Variance projection function and its application to eye detection for human face recognition, International Journal of Computer. Vision. 19 (1998) 899906.

[11] G.C. Feng and P.C. Yuen, Multi-cues eye detection on gray intensity image, Pattern Recognition. 34 (2001) 10331046.

[12] S. Kawato and J. Ohya, Real-time detection of nodding and head-shaking by directly detecting and tracking the between-eyes, Proceeding 4th IEEE Int. Conf. on Automatic Face and Gesture Recognition, 2000, pp. 4045.

[13] Y. Tian, T. Kanade and J.F. Cohn, Dual-state parametric eye tracking, Proceeding 4th IEEE Int.ernational Conference on Automatic Face and Gesture Recognition, 2000.

[14] S.A. Sirohey and A. Rosenfeld, Eye detection in a face image using linear and nonlinear filter, Pattern Recognition 34 (2001) 13671391.

[15] G. Chow and X. Li, Towards a system for automatic facial feature detection, Pattern Recognition 26 (1993) 17391755.

[16] H.A. Rowley, S. Baluja and T. Kanade, Neural network-based face detection, IEEE Transaction Pattern Analysis Machine Intelligent.20 (1) (1998), 2338.

[17] K.K. Sung and T. Poggio, Example-based learning for view base human face detection, IEEE Trans, Pattern Analysis Machine. Inteligent. 20 (1) (1998) 3951.

[18] P.C. Yuen, G.C. Feng and J.P. Zhou, A contour detection method: initialization and contour model, Pattern Recognition Letter 20 (2) (1999) 141148.

[19] K. Sobattka and I. Pitas. "A Novel Method for Automatic Face Segmentation. Facial Feature Extraction and Tracking," Signal Processing:Image Communicarion, 12(3). 1998, 263-281.

[20] Huang, T.S., G.J. Yang, and G.Y. Tang, A Fast Two-Dimensional Median Filtering Algorithm. IEEE Transactions on Acoustics, Speech, and SignalProcessing, 1979. ASSP-27: p. 13-18.

[21] R. Gonzalez and R. Woods, Digital Image Processing; Prentice Hall 2002.

[22] http://agingmind.cns.uiuc.edu/facedb/

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