DETECTING DRIVER S EYES DURING DRIVING
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1 DETECTING DRIVER S EYES DURING DRIVING Jung-Ming Wang ( 王 俊 明 ), Hui-Wen Lin ( 林 慧 雯 ), Chiung-Yao Fang ( 方 瓊 瑤 ), and Sei-Wang Chen ( 陳 世 旺 ) Department of Information and Computer Education Graduate Institute of Computer Science and Information Engineering National Taiwan Normal University, Taipei, Taiwan, Republic of China 106 Tel:(886) #103 Fax:(886) schen@csie.ntnu.edu.tw ABSTRACT Since car accidents may be caused by the driver's tiredness, there are some assistance systems designed for bringing the attention of a driver. In such a system, monitoring the driver s eyes plays an important role in understanding the driver s status. We propose a vision-based eyes detection method in this paper. Two steps, human face extraction and eyes location, are consisted in our method. Human face is extracted based on the skin color analyzing, and their eyes are then located according to the features of eye. Our system has been tested in the real driving, and shows that it can be adaptive to the illumination changing in vehicle motivation. Keywords: Face detection, eye detection, mixture of two-gaussian, restoration 1. INTRODUCTION Even each driver thinks that he can control the vehicle very well in any time, there still are some risks happened because of the drivers' tiredness, drowsiness, or inattention. In order to prevent their being tired in the driving, some driver assistance systems have been developed to bring the attention of a driver. There are three kinds of techniques used for detecting a driver's consciousness. The first of all is to detect the driving state [13][15], such as the change of speed, the frequency of turning wheel, or the frequency of braking. Since the traffic cases are so complex, it is not correct enough for real applications. In the second method, we may detect the driver's mental status by the medical instruments [1][16]. Even the detecting results is better than the first method, however, the driver should be asked to wear some instruments in their driving, This work is supported in part by the National Science Council, Taiwan, Republic of China under contract NSC E which will influence their driving and is hard to implement. In the last method, we may install a camera to capture the driver s image continually, and then based on these images to detect their eyes blinking. This is because some researches [4][7] show that eyes blinking have strong relationship with the medical status. In addition of that, this method is more applicable than the others mentioned above. Since detecting a driver's eye is more applicable than the others, there are many approaches for eyes detection methods proposed in the recent years [9] [10] [14] [15]. In this topic, face detection and eyes locating are often the two main steps. For face detection, there are about three kinds of methods could be utilized, such as machine learning (neural networks [19], principal component analysis [22], support vector machines [21], Kullback-Leibler boosting [20], Gaussian mixtures [25]), shape fitting (ellipse fitting [34], geometrical modeling [26], template matching [27]), and color analysis. For surveys on face detection, we may see [17] and [18]. In the above methods, the machine learning methods need many training data for applying, and the shape fitting methods need more computation than the others, so the color analysis method is more applicable than the others. The only problem is that it could be influenced by the illumination, and the vehicle moving conditions cannot help to face this problem. Instead of the traditional color spaces (ex. RGB or HSI), some color spaces [2][8] have been proposed for solving, and showed that would be not influenced for skin color in different illumination. For all of these color spaces, YCrCb color space is the most famous. This is because the skin color in this space has a clear range as that shown in Fig. 1 [17]. Furthermore, Chai et al. [2] indicate that the skin color of the different human beings would locate Cr and Cb value in a specific range. However, in the strong illuminating environment, all color spaces would be failed for detecting skin color, even if the YCrCb space. Some approaches, such as referencing the PCA values [23], redefining the YCrCb ranges [17], [30], comparing YCrCb with RGB spaces at the same time [29], or designing new color spaces [11], could be applied to solve these problems. Lievin et al. 941
2 Fig. 1. The YCrCb color space (blue dots represent the reproducible color on a monitor) and the skin tone model (red dots represent skin color samples): the YCrCb space; a 2D projection in the Cb Cr subspace. [17] proposed a new color space, LUX, designed from YCrCb could be used to separate color information from the illumination, and is useful for detecting skin color in the strong illumination. In this paper, YCrCb color space is combined with the LUX to extract the range of human face. After face detection, eyes can be extracted based on their features, such as the position, shape, and color. Because some features would be changed continuously in driving, some researches [8][11] show that locating eyes with a tracking technique will have a better result. Based on a tracking technique, the driver's blinking will then be detected. The rest of this paper is organized as follows. In the next section, the LUX with YCrCb color spaces will be introduced. The section 3 will address on the architecture of our method. The human face and eyes detection steps are addressed on the section 4 and section 5 respectively. There are some experimental results in the section 6 and some conclusions are given in the section 7. (f) Fig. 2. Comparing the extracting result based on different color spaces;, input images captured at nightfall; extracting face region based on Cb and Cr components from ; extracting face region based on Cb and U components from, sensitive to red color; (f) extracting face region based on Cb and U' components from. LUX color space is also useful for detecting the skin regions [11]. It is a nonlinear color space based on a logarithmic transform from YCrCb color space. However, according to our test, it is very sensitive to red color, so that more than skin regions will be extracted at the same time (Fig. 2). In our method, an U' component modified from the U component is defined as the following equation. 2. COLOR SPACE As mentioned before, the skin color is an important feature for extracting human face in color images. However, the skin color would be different in various illuminations and human beings. Since YCrCb color space separate the chrominance from the luminance, it is useful for detecting the skin area. The equation of RGB space to YCrCb space is shown in equation (1). Y R 1 + (1), Cr = G 128 Cb B 128 where R, G, and B mean the red, green, and blue values in RGB color space. Even many researches show that YCrCb color space is good enough for detecting the skin area, our experiments shows that it will be influenced while the illumination changing in the outdoors. There are some examples shown in Fig. 2. In this example, the human face will be extracted incompletely at nightfall while only the Cr and Cb components are combined for detecting the skin pixels. G R 256 if < 1. 5 and R > G > 0, U' = R G 255 otherwise. (2), where G and R are the green and red values in RGB color space respectively. Combining Cb and U' components, face region will be extracted well as that shown in Fig. 2. The combing method will be discussed in the section ARCHITECTURE In this system, one camera is installed in the front of the driver (Fig. 4), and captures the driver s face during driving as shown in Fig. 4. The video sequence is then applied with our face detection and eye detection, and the flowcharts are shown in Fig. 3 and Fig. 9 respectively. In the face detection, an input image is sub-sampled into a smaller one for saving processing time and removing some noise. The skin color pixels are then detected according to their Cb and U' components. By applying connected component labeling, the largest region can be extracted and considered as the face region. 942
3 Fig.4. The camera set; Input image; U' component; Cb component; Result of combining and, skin region, as black points. Fig. 3. The flowchart of face detection. Because the face region would be broken in the high illumination (Fig. 6), a recovery method referring with color and edge information is then applied here for recovering the actual face region. At last, an ellipse is used to fit the face region and considered as our face detection. The details of the face detection step will be discussed in the next section. In the eye detection, a block region of the eyes possible location is defined on this ellipse detected before. Those pixels that do not belong to the skin color pixels in this region are detected, and then apply the connected component algorithm to detect some larger regions. All of these regions are considered as the eye candidates, and their features are extracted for judgment. Each eye candidate is given a confidence value after analyzing its features, and the confidence values will be summed up with the other one of the same region in the next frame by tracking. Two eye candidates with confidence value larger than a threshold predefined would be the eyes regions what we want. The details of the eye detection step will be discussed in the section FACE DETECTION In the sub-sampling images, I, each pixel s U' and Cb components are extracted by the equation (1) and equation (2). Those pixels with values in the ranges R Cb and R U' for Cb and U' components respectively would be Fig. 5. The first row is skin region including noise, and the second row is face and neck region selection based on the largest skin region. skin color pixels. A new image, F S, with skin color pixels marking will be created as described in the following equation: F (x,y) = 1, F (x,y) R,F (x,y) R Cb Cb U' U' (3), s 0, otherwise were F Cb (x,y) and F U (x,y) are the Cb and U' values of pixel at position (x,y). In our test, R Cb is between 77 and 127, while R U' is between 0 and 249. There is an experimental result shown in Fig. 4. Connected component labeling is then applied to detect all of the skin regions in F S, and we calculate their size at the same time. The largest connected component, called as the skin region, will be preserved in F S as shown in Fig. 5, and the F S is called as the skin map in this paper. In the strong illumination environment, a human face region will be broken seriously by the above method, such as show in Fig. 6. In order to detect the skin region more correctly, we create the edge map, F E, by applying the Sobel edge detection method for I, and an example of the edge map is shown in Fig. 6. If a pixel at (x,y) is not in the skin region in F s and not an edge in F E, we check its eight neighbors in F s whether any one of them is in skin region, and average those corresponding 943
4 Fig. 7. The ellipse, E, fitting the face. The relationship of the ellipse to the location of eye block. Fig. 6. Input image under strong light, Skin region based on Cr and Cb component. Skin region based on U and Cb component. Sub-sample of, called skin map, Edge map of, (f) Restoration of the largest skin region. (f) Fig. 8. Different faces have different ratios of the height to the width. pixels values in I. If the value of the pixel at (x,y) in I is largely different than the average value, F s (x,y) is updated to 1 as a skin color pixel. After checking all of the pixels in F s repeatedly, skin region will be recovered as shown Fig. 6(f). Since the shape of human face is similar to an ellipse, we try to use an ellipse to fit the driver s face. There are three parameters for an ellipse model, major axis a, minor axis b, and the center position (x, y ). The center position, (x, y ), is calculated by averaging all of the position values of the pixels in the skin region: x = 1 x, R S F S (x,y) R S y = 1 y, R S F S (x,y) R S where R S means the skin region and R S means its size. Pixels on the top boundary of the skin region are detected by scanning the R1 area as shown in Fig. 7, and their y positions are averaged as the top of the ellipse. Then the major axis, a, is the distance from the top to the center position, (x, y ). In the same works, scanning the R2 and R3 areas will obtain the left and right boundaries, X left, X right, of the skin region. Half of distance (X right X left ) can be the minor axis, b. Now we have all parameters to define our face model by an ellipse. However, the skin region often contains the human s neck, and cause the center position, (x, y ), is not exactly on the center of the face. Because of that, we update the major axis, a, and the center position based on the minor axis, b, by the following rule: 1.1b,if 0.8b < a < 1.2b; a = 1.2b,if 1.2b < a < 1.5b; (4),,if 1.5b < a 1.5b and X left + X right x' = ; 2 (5). y' = y' a The equation (4) is defined because the different faces have different ration of their height to the width as shown in Fig. 8. Then we will obtain a more suitable ellipse to fit the face shown in Fig EYE DETECTION The flowchart of the eye detection is shown in Fig. 9. A block region of the eyes possible location is defined on the face model shown in Fig. 7, where the height of the block is 0.8 a, and the width is 2 b. The area of the block in I is then extracted as the eye block. There are some results shown in Fig. 10. Since the pixels belong to the eyes or eyebrows are not skin colors, there are some holes in the skin region, and we call these holes are non-skin components. The non-skin components are applied some feature detection method for judging them being an eye candidate. Those features used here are the size, the ratio of the height to 944
5 Fig. 10. The experimental results of extracting the eye blocks from input images. Fig. 9. The flowchart of eye detection. the width, the number of black pixels, and the position. A non-skin component is considered as an eye candidate if its size is larger than a threshold and smaller than the quarter size of F B, its ratio of the height to the width of the non-skin component is larger than a threshold, and its number of the black pixels is larger than a threshold. The eye candidates are marked in squares as shown in Fig. 11. The horizontal line in the middle of an eye candidate is represented as a histogram, F(x), shown in Fig. 12, where the x-axis is the corresponding position on the line and the y-axis is the pixels gray values. Since there are two peaks in the histogram of an eye block, we apply the mixture of two-gaussian, G(x), [35] to determine the peaks of each histogram: G(x) = G 1 (x) + G 2 (x) (6). where G 1 and G 2 are the two Gaussian functions with means, µ 1 and µ 2, and variances, σ 1 and σ 2. Assume that there is a threshold value x m, µ 1 and σ 1 can be calculated from F(x), x<x m, and µ 2 and σ 2 can be calculated from F(x), x x m in the same words. The best threshold value is x m selected while F(x)-G(x) is smaller than the other ones. The mixtures of two-gaussian with their corresponding histograms are shown in Fig. 13. If the two peaks in the mixture of two-gaussian are similar to the peaks in the histogram, this eye candidate is then considered as an eye block. Compare each eye candidates with those in the previous frame to find out their correspondence. The confidence values of those eye candidates determined to be eye blocks are increased during time passing. Those eye candidates will be considered as eye components while their confidence values are greater than a threshold value. (f) Fig. 11. the eye candidates (eyes are marked in squares.) in the frame at time t-1; the eye candidates at time t. 6. EXPERIMENTAL RESULTS The proposed approach has been tested on an Intel P4 PC with 2.8 GHz. The driver s eyes could be detected as shown in Fig. 14, under strong light or standard light. Fig. 15 shows the detection of the drivers eyes during driving. 7. CONCLUSIONS We can detect those pixels with skin color by combing the information of YCrCb and LUX color spaces. Since the strong illumination would destroy this information, we can recovery the skin region by analyzing their edge information in advance. Our experiments show that skin region will be extracted robustly under our designed method. After face detection, the human s eyes can be located according to their features and are tracked to confirm the detection results. The experiments show that our purposed method could be applied to detect the drivers' eyes during their driving. In our future works, the blinking will be detected based on their position. The detection results will help to understand the driver s status, and help the driver assistance system to bring attention of a driver. REFERENCE [1] J. L. Cantero, M. Atienza and R. M. Salas, Human Alpha Oscillations in Wakefulness, Drowsiness Period, and REM Sleep: Different Electroencephalographic Phenomena within the Alpha Band, Neurophysiol Clin 32:54 71,
6 Fig. 12. The eye block with eye candidates (eyes are marked in squares); The mid-line of eye candidate 3 is the interest line (as white line); The intensity values of the interest line of. (f) (g) (h) (i) Fig. 13. Detection of classify eyes and non-eyes. four eye candidates in an eye block;,,, and are the histograms of eye candidates1, 2, 3, and 4 respectively. (f), (g), (h), and (i) are the mixture of two-gaussians of,,, and respectively. [2] D. Chai, K. N. Ngan, Face Segmentation Using Skin-Color Map in Videophone Applications, IEEE Trans on Circuits and Systems for Video Technology, vo.9, no.4,pp , June [3] C. Conceicao, R. Silva and R. Guerreiro, Early Detection of Drowsiness Using a Neuro-fuzzy Detector, Workshop on Biomedical Engineering 27 Setembro, pp 82-86, Dec [4] D. F. Dinges, M. M. Mallis, G. M. Maislin and J. W. Powell, Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and the Basis for Alertness Management, Dept. Transp. Highway Safety, pub , April [5] A. Eskandarian and R. Sayed, Driving Simulator Experiment: Detecting Driver Fatigue by Monitoring Eye and Steering Activity, Proc. of NDIA Symp. on Annual Intelligent Vehicles Systems, June [6] R. Grace, V.E. Byrne, D.M. Bierman, J.-M. Legrand, D. Gricourt, B.K. Davis, J.J. Staszewski and B. Carnahan, A Drowsy Driver Detection System for Heavy Vehicles, Proc. of AIAA/IEEE/SAE 17th DASC Conf. on Digital Avionics Systems, vol.2, pp.i36/1-i36/8, 31 Oct.-7 Nov [7] T. Hayami, K. Matsunaga, K. Shidoji and Y. Matsuki, Detecting Drowsiness while Driving by Measuring Eye Movement - a Pilot Study, Proc. of IEEE 5th Int'l Conf. on Intelligent Transportation Systems, pp , Singapore, Sept Fig. 14. The experimental results of detecting the driver s eyes, eyes are marked in squares. [8] W. B. Horng, C. Y. Chen, Y. Chang and C. Hai Fan, Driver Fatigue Detection Based on Eye Tracking and Dynamk, Template Matching, Proc. of IEEE Int l Conf. on Networking, Sensing and Control, vol. 1, pp.7-12, Taipei, Taiwan, March [9] T. Ito, S. Mita, K. Kozuka, T. Nakano and S. Yamamoto, Driver Blink Measurement by The Motion Picture Processing and Its Application to Drowsiness Detection, Porc. of IEEE 5th Int l Conf. on Intelligent Transportation Systems, pp , Singapore, Sept [10] Q. Ji, Z. Zhu, P. Lan, Real-time Nonintrusive Monitoring and Prediction of Driver Fatigue, Trans. of IEEE on Vehicular Technology, vol. 53, no. 4, pp , July [11] M. Lievin and F. Luthon, Nonlinear Color Space and Spatiotemporal MRF for Hierarchical Segmentation of Face Features in Video, IEEE Trans. on Image Processing, vol. 13, no. 1, pp , Jan
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