JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, VOLUME 3, ISSUE 5, MAY 2013 1

A Robust Hybrid Design for Driver Fatigue Detection

Ijaz Khan, Hadi Abdullah and Mohd Shamian Bin Zainal

Abstract— This paper presents a robust design for fatigue detection that analyse yawning and eyes status of driver to detect driver’s level of vigilance. This system consists of seven stages starting from face detection. Combination of Viola Jones and skin color faces detection techniques are used for face detection in parallel that makes the design more accurate and fast. Both techniques are applied to detect face simultaneously. The technique that first detects face passes the image to next level, while image processed by second techniques is discarded. Snake counter is applied to track the detected face. Eyes and mouth are detected by using eye and mouth maps. To avoid complicated calcula-tions to detect eyes and mouth in each image a snake counter is applied to track them continuously. The system uses skin color pixel information and a threshold to differentiate between eye and closed eyes. Using variation information of snake counter the system detects yawning state of driver. Yawning and closed eyes detection combined with two face detection techniques not only makes this system fast but also provides accuracy.

Index Terms— fatigue detection, face detection, yawning analysis, eyes status analysis, snake counters .

—————————— u ——————————

1 INTRODUCTIONRVING in drowsines have long been acknowledged to be one of the main hazard in safe driving.

Drowsiness has a negative imapact on driver’s abitiltes of driving and impairs driver’s judgment and quick response time. The National Highway Traffic Safety Administration (NHTSA) estimates that 100,000 police-re- ported crashes are the direct results of driver fatigue each year[1].This results in an estimated 1550 deaths, 71,000 injuries, and $12.5 billion in monetary losses. Based on researches done by the Real Automóvil Club de España (RACE), driver drowsiness involves a high percentage (30%) of traffic accidents[2].According to national sleep foundation 2005 in America poll, 60% of adult drivers –about 168 million people- say they have driven a vehicle while feeling drowsy in the past year and more than one third (37% or 103 million people) have actually fallen asleep at the wheel. Therefore an assistive system inside vehicle that monitors driver’s lever of vigilance while he is drowsy, which can alert the driver after detection drowsiness is essential to prevent road side accidents.

Researchers have been developing many techniques for drowsiness detection. Some of major techniques are shown in figure 1.

These techniques can be classified into 3 categories. First is physiological approach in which drowsiness is de-tected by recording brain activates in alpha and gamma

band EEG (Electroencephalography) [3],[4],[5],blood pres-sure ,heart rate variability[6] and recording eyes movements EOG (Electrooculography) [7],[8],[9],[10],[11]. However sensitivity of physiological approach is still low compared with visual data. The second is driving behavior in which data observed from vehicle and road such as longitudinal and lateral speeds, accelerations, steering wheel angle are monitored[12],[13].Different equipments are used to acquire

—————————————— • Ijaz Khan is a researcher of Electrical engineering at University Tun Hus-

sein Onn Malaysia (UTHM), Batu Pahat, Malaysia. • Hadi Abdullah is a researcher of Electrical engineering at University Tun

Hussein Onn Malaysia (UTHM), Batu Pahat, Malaysia. • Shamian Bin Zainal is head of laboratory-faculty of electrical and electro-

nices engineering at University Tun Hussein Onn Malaysia (UTHM), Ba-tu Pahat, Malaysia.

D

Fig.    1.  Driver  fatigue  detection  techniques.  

2

such data from vehicles and roads. However applications of exotic devices in this approach are limited to vehicle type and road conditions. The third approach is to detect drowsi-ness from facial expression such as yawning and eyes clo-sure[14],[15],[16],[17].Numerous results demonstrates that facial expressions provides rich and reliable information about drowsiness[18]. In this paper we propose a hybrid drowsiness detection system which takes real time imag-es of driver which are processed in four phases shown in figure 2. In first phase face is detected using hybrid method of Viola Jones and skin color detection technique (discussed in sec-tion 2.1 and 2.2). A snake counter is applied for continuous face tracking in phase 2(section 2.3). In 3rd phase mouth and eyes are detected and snake counter is applied to track eyes and mouth. To monitor yawning state and state of eyes (closed or open) snake counter and skin color pixel detection techniques are used (discussed in section 3). Once driver is observed in yawning state or with closed eyes for more than two seconds an alarm will on or a verbal statement will be given by the system advising driver to drive carefully. Using hybrid of Viola Jones and skin color detection for face de-tection not only increases accuracy but also decreases time required to detect face. Also a combination of yawning de-tection and eye state detection gives our system a promising solution for drowsiness detection.

2 FACE DETECTION AND TRACKING TECHNIQUES Face detection techniques can be categorized into two major groups that are feature based approaches[19],[20] and image based approaches. Image based approaches use linear subspace method, neural networks[21],[22],[23] and statistical approaches for face detection. Feature based approaches can be subdivided into low level analy-sis, feature analysis and active shape model. Figure 3 demonstrate techniques and methods used to detect face in an image.

2.1 Viola Jones Method The basic principal in viola Jones[24] method is to scan a sub window that can detect a face in an input image. Vio-la Jones detector runs several times across an image with different size. This detector requires same number of cal-culations regardless of the size of the image which makes it faster than the standard image processing approach that on the contrary rescale the input image size and then

Fig.    2.  Fatigue  detection  phases    

Fig.    3.  Face  detection  techniques.  

Fig.    4.  Viola  Jones  integral  image  construction.  

3

⎪⎩

⎪⎨⎧ >

=otherwise

pxpfpfxh

1)(1

),,,(θ

θ

run a fixed size detector. Viola Jones detector is construct-ed of integral image and haar-like features shown in Fig-ure 4 and 5.

An image is first converted to integral image, which is done by summing up all the pixels above and to the left

of the concerned pixel, and then set entire pixels equal to that sum. Sum of rectangle ABCD= D-(B+C)+A.

The detector analyzes sub window with the help of

features that consist of two or more than two rectangles.

To get the single resultant value of each feature, the sum of the white rectangles are subtracted from the sum of the black rectangles. Figure 6 shows application of haar-like feature on actual face image. For feature selection viola Jones used AdaBoost[25],[26] to build a simple classifier from computationally efficient features. Adaboost is a machine learning boosting algorithm that constructs a strong classifier from weak classifier.

A weak classifier can be described mathematically as (1) Where is polarity, is applied feature, is a sub

window and ! is threshold. It is obvious that most of the region in an image consist

of non-face. Viola Jones detector detects the non face area in an image and discards that area which results in detec-tion of face area. For this purpose viola jones take ad-vantage of cascading. When a sub window is applied to cascading stages, each stage decides whether the sub window is a face object or not. Sub windows which are not face are discarded and those which contain some per-centage of having face objects are passed to next stage. Final stage is considered to have a high percentage of face objects. Higher number of stages in cascading provides more efficient result of detecting face in a sub window. Figure 7 shows cascading stages used in Viola Jones method.

Fig. 7. Cascading stages used in Viola Jones Method to discard non-

faces.

2.2 Skin color detection technique The second method used for face detection is based on

skin color face detection method. In face detection ap-proaches detection of skin color is first step. Major ad-vantages of this technique are its robustness, non sensitiv-ity to position and shape invariance. However for this technique to work, it is vital to use an accurate color space model. Some existing color spaces are RGB, CMY, XYZ, UVW, LSLM, L*a*b*, L*u*v*, LHC, LHS, HSV, HSI, YUV, YIQ, YCbCr[27],[28] out of which Most commonly RGB, HIS , YCbCr are used. We are using RGB color space for skin color detection as it is native representation of color

Fig.    5.  Viola  Jones  Haar  like  features.  

Fig.     6.   Viola   Jones   Haar   like   features   applied   on   actual   face  

image.  

4

⎭⎬⎫

⎩⎨⎧ ++= )()()(31 22

r

brb C

CCCEyeMapC

images and it is widely used for processing and storing digital images. RGB color space consist of three basic col-ors R (red), G(green), B(blue) that can be combined to produce any resultant color. Although different people have different skin colors, studies have shown that actual difference lies between the intensities[29]. So if Brightness is removed from color representation, the difference be-tween human skin colors can be reduced. In order to de-tect skin color following set of rules have been found to be more accurate than other models.

(R>95) AND (G>40) AND (B>20) (first condition) AND (max {R, G, B} – min{R, G, B} > 15) AND (|R-

G|> 15) AND (R>G) AND (R>B) AND (R>220) AND (G>210) AND (B>170) AND (R>B) AND (G>B) (2)

Pixels of RGB image are detected as skin if first condi-

tion holds true. Rest of the conditions are used to ensure that RGB components must not be close together, that ensures grayness elimination. It also ensures that R and G component must not be together which must be true for fair complexion

R is largest in R, G and B for pixel of skin color regions in RGB color space and B maybe be larger than G for pix-els of skin color regions in the shadow. These rules are suitable for detection of skin color under regular illumi-nation conditions as well as in shadow. The resultant im-age from this technique is a black and white image in which skin is converted to white and rest of the colors are converted to black. Face can be detected then by cutting the biggest white connected area with in black and white image.

2.3 Snake Counter for face tracking The snake model (active counter model) was introduced by Kass et al [30]. It was used to solve the problem of tracking human’s face and other complex shapes and has been reported to successful track humans head and face boundaries [31]. Snakes are curves defined within an im-age domain that can move under the influence of internal and external forces. Internal force comes from within the curves itself while external forces are computed from the image data. The internal force imposes a piecewise smoothness constraint while the image forces push the snake towards the salient image features, for instance edges, lines or contours. A snake performed in the image could be represented by a set of snaxels, Vi=(Xi,Yi) for I=0,…,N-1 where the X and Y coordination of the snaxel I are denoted as Xi and Yi respectively. The energy func-tion could be written as

(3)

E internal represents internal energy and can be com-

puted as the sum of the snake contour continuity energy and the contour curvature energy.

(4)

(5)

(6) Where D is known as the average distance between all

the pairs from the snaxel set. External represent the external energy which moves the snake to the feature of interest in an image such as boundaries. An external energy equation is

(7)

Where is a grey level image and is the gradi-

ent operator.

3 SYSTEM DESIGN The drowsiness detection system is based on seven levels which can be classified as:

• Face detection • Face tracking • Eyes detection • Mouth detection • Eyes and mouth tracking • Eye status analysis • Yawning detection

A detailed flow chart of the system is given in figure 8.

3.1 Face detection Face detection is accomplished by using two methods; Viola Jones and Skin color detection. To make the design robust these two methods are used simultaneously. Whichever detects face first sends it to next level and oth-er image processed by any of these methods is discarded. This not only makes system faster but also gives high ac-curacy of both methods.

3.2 Face tacking Since this system is designed to take real time images of driver, it will be very slow and time consuming if the sys-tem applies face detection on every image it takes. Snake counter is applied to track face after detecting it. And once face tracking is lost by snake counter it is again de-tected in previous level.

3.3 Eyes detection To detect eyes in the face, we built an eye map that is based on the chrominance component [32]. The eye map is generated by observing that high and low values are found around the eyes. Eye map can be generated by equation (8). (8)

Once the positions of eyes are highlighted by eye map, the image then can be converted to black and white im-age, in which eyes are denoted by white color pixels.

5

( ) ( )2

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b

rrr C

CCCMouthMap η

( )

( )( )

( )∑

∑

⎟⎠⎞

⎜⎝⎛

=

yx b

r

yxr

yxCyxC

n

yxCn

,

),(

2

,,1

,1

95.0η

Since eyes are located in upper half of the face there for

to reduce our calculations and to reject false alarm we only consider the upper part of the black and white im-age. Eyes then can be detected based on their geometrical shape and biggest connected component of white pixels.

3.4 Mouth detection In order to find mouth in the face, a mouth map can be generated based on the strongest differences in the color of face area and mouth region. Since mouth area contains strongest red component and weaker blue component [32] , hence the chrominance component will be less then in mouth area. Using equation (9 and 10) we can generate mouth map.

(9)

(10)

The image will be converted to black and white after

the mouth area is highlighted by the mouth map. Since mouth is located in the lower half part of the face, hence the lower half part of the image will be taken into consid-eration only. Mouth can be detected as the biggest white globe in the lower part of the image.

3.5 Eyes and mouth tracking Once mouth and eyes are detected in the face area, it will be cumbersome and time consuming to detect mouth and eyes each time in new image, therefore to reduce such calculations snake counters (active counters) are applied to mouth and eyes which will continuously track them in the image. The dynamic update feature of the snake con-tours makes it an appropriate choice for mouth and eyes tracking. Additionally the snake counter algorithm is fast enough to track eyes and mouth in real time environ-ment.

3.6 Yawning detection Yawning while driving depicts that driver is getting lousy therefore detection of yawning is most important step in detection of drowsiness. During yawning the area of mouth increases vertically so when area of mouth in-creases the area of the applied snake counter will increase in sub sequent frames as well. The state of yawning is detected if the area of the snake counter increases vertical-ly from its normal size which is already calculated when

Fig. 8. System design flowchart showing application of different methods and techniques to detect drowsyness.

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snake counter was applied to the mouth in normal state. Fiqure 9 demonstrates snake counter applied on mouth in normal and yawning state.

3.7 Eye status detection When eyes are tracked using snake counter, skin color detection technique is applied to monitor state of eyes weither open or closed. When eyes are closed number of skin color pixel will increase compared to open eyes be-cause colors of eyes are not skin (Sclera is mostly com-posed of white and some red part and Iris black, brown, grey etc no skin). Open and closed eyes detection using skin color technique can be seen in figure 10. To detect drowsiness from eyes the system must be able to differen-tiate between eye blinking and closed eyes due to sleepi-ness. Since human normal eye blinking takes less than a second the system threshold is set to two seconds. Whenever system detects closed eyes for more than two seconds it will alert the driver of being drowsy.

4 SIMULATION RESULTS The simulation of drowsiness detection system is done in on a computer with specifications given in table 1.

TABLE 1

Specification and simulator used.

System Parameters Values and versions

Image size 640X480 Pixels

Simulation Software Matlab 7.1R2012a

Processor Intel(R) core(TM)i5-2450(2.50GHzX2)

RAM 4GB

Operating System Windows 7( 64bit)

The system performance with six different cases their

time of detection and system selection is given in detail in table 2 and figure 11.

In first image shown in figure 11 (a), (b) and (c), (a) is face detection using Viola Jones which is accomplished by this method using system parameters given in table 1 in 0.946s. While the same image processed by skin color de-tection with same system parameters is done 1.132s shown in figure 11(b). Since Viola Jones technique detect-ed image first, the system will take that image and pass it to next level where snake counter is applied. The differ-ence of time is not much but drowsiness detection case of driver even this small duration can make increase system performance and help driver form any accident possibil-ity.

Fig. 9. (a) Snake counter applied on mouth to track mouth while driver is moving his head. (b) Detection of yawning by observing area of snake counter which increases verti-cally

Fig. 10. Closed and open eyes identification using Skin color detection. (a) Shows actual open and closed eyes, (b) shows processed skin color technique image where white part shows non skin pixels and black shows skin color pixels.

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TABLE 2 Face detection using Viola Jones and Skin color detec-

tion, their detection time and system selection.

Image Viola Jones Skin Color Detection

System Selec-tion

Image (a),(b),(c) in Figure 11

Image (a) Detection

time= 0.964s

Image (b) Detection

time= 1.132s

Image(c) Viola Jones

selected

Image (d),(e),(f) in Figure 11

Image (d) Detection

time= 1.019s

Image (e) Detection

time= 1.034s

Image(f) Vio-la Jones se-

lected

Image (g),(h),(i) in Figure 11

Image (g) Detection

time= 1.105s

Image (h) Detection

time= 1.059s

Image(i) Skin Color selected

Image (j),(k),(l) in Figure 11

Image (j) Detection

time= 0.9654s

Image (k) Detection

time= 0.983s

Image(l) Vio-la Jones se-

lected

Image (m),(n),(o) in

Figure 11

Image (m) Detection

time= 0.990s

Image (n) Detection

time= 1.119s

Image(o) Viola Jones

selected

Image (p),(q),(r) in

Figure 11

Image (p) Detection

time= 0.985s

Image (q) Detection

time= 0.910s

Image(r) Skin Color

selected

Fig. 12. Eyes and mouth detection of image selected by system.

Fig. 11. Viloa Jones and Skin Color Detection techniques applied on images and system selection.

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The images that are selected by the system are then send to next levels in which eyes and mouth are detected (dis-cussed in section 3.3 and 3.4). Figure 12 shows images with eyes and mouth detection. After these detections Snake counter is applied to track mouth and eyes contin-uously.

The system then continuously monitors eyes and mouth. And eyes status and yawning condition are de-tected (discussed with examples in 3.6 and 3.7).

The overall accuracy of system in average of all seven levels (face detection, face tracking, eyes detection, mouth detection, eyes and mouth tracking, yawning detection and eye status detection) using system parameters given in table is 96.7%. The highest accuracy of system design is shown in first level i.e. face detection.

5 CONCLUSION In our study we have designed a drowsiness detection system with increased efficiency, accuracy and speed by using two different methods for face detection. To make design faster snake counters are applied on face eyes and mouth which decreases processing time of detecting face, eyes and mouth in each real time image. Yawning in image is detected by taking information by variation in size of snake counted that is applied on mouth. To check status of eyes a threshold value is applied with skin pixel detection. This threshold dif-ferentiates between eyes blinking and closed eyes due to sleepiness. This design can be easily implemented using FPGA and a simple VGA camera placed on the base of steering wheel. With further improvement in different levels the system can have more accuracy and become a more practical product.

ACKNOWLEDGMENT The authors wish to thank Almighty Allah. The work was supported by University Tun Hussein Onn Malaysia.

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Ijaz Khan joined University Tun Hussein Onn Malaysia in 2012 as a student of M.S Electrical engineering. He did his Bachelor in Electri-cal engineering from COMSAT Abbottabad, Pakistan. His major re-search focus is FPGA designs of security systems, Wireless intruder tracking systems using FPGAs, economical solutions for large scale monitoring systems and face monitoring systems as road safety feature in automobiles. Hadi Abdullah received his bechlour degree in Electrical engineer-ing from COMSAT Abbottabad, Pakistan in 2011. In 2012 he joined University Tun Hussein Onn Malaysia as a research student of M.S in Electrical engineering. The major fields of his research are wire-

less senor networks, Ultra Wide Band applications and FPGA design of wireless radar as a road safety feature in automobiles. Mohd Shamian Bin Zainal received his Ph.D degree in Electrical engineering form Hokkaido University Japan in 2010. He did M.S also in Electrical engineering form University Tun Hussein Onn, Ma-laysia in 2003. His Bachelor is from University technology Malaysia 2001. He accepted position of Head of Laboratory - Faculty of Electrical Engineering and Electronics in University Tun Hussein Onn Malay-sia. Also he is senior lecturer teaching different subjects of Electrical engineering at UTHM since 2004. He currently supervises M.S re-search students in different research projects, mostly related to FPGA, Real Time Embedded Systems and Communication. His current research is Smart Sensor For Inhaler, New Approach Of Hardware Modeling By Using FPGA For Cardiac Reentrant Arrhyth-mia Real-Time Analysis Tool, Radar designs for high precision locali-zation of intruder in high security area, FPGA based face tracking and observation for driver’s drowsiness detection and road monitor-ing. Journal: Mohd Shamian, Shingo Yoshizawa, Yoshikazu Miyanaga, ``Ultra-Low Power and Large Scale Design of Sub-threshold Digital Circuits for Wireless Communication Systems,'' RISP Journal of Signal Processing, Vol. 13, No.6, pp.487-496, Nov. 2009. Learning modules: Microprocessor and Microcontroller. He is a member of the IEEE and the IEEE Computer Society.