Real Time Face Detection using GeometricConstraints, Navigation and Depth-based Skin

Segmentation on Mobile RobotsVo Duc My

Cognitive Systems GroupComputer Science Department

University of TuebingenSand 1, D-72076 Tuebingen, GermanyEmail: duc-my.vo@uni-tuebingen.de

Andreas MasselliCognitive Systems Group

Computer Science DepartmentUniversity of Tuebingen

Sand 1, D-72076 Tuebingen, GermanyEmail: andreas.masselli@uni-tuebingen.de

Andreas ZellCognitive Systems Group

Computer Science DepartmentUniversity of Tuebingen

Sand 1, D-72076 Tuebingen, GermanyEmail: andreas.zell@uni-tuebingen.de

Abstract—Face detection is an important component for mobilerobots to interact with humans in a natural way. Various facedetection algorithms for mobile robots have been proposed;however, almost all of them have not yet met the requirementsof the accuracy and the speed to run in real time on a robotplatform. In this paper, we present a method of combining colorand depth images provided by a Kinect camera and navigationinformation for face detection on mobile robots. This method isshown to be very fast and accurate and runs in real time inindoor environments.

I. INTRODUCTION

Face detection is a necessary step for many other algorithmsof face analysis such as face recognition, face tracking andfacial expression recognition. With increased development ofsensor technology, robots will be equipped with more andmore different sensing modules, such as laser range scanners,sonar sensors and Kinect cameras. Among them, the Kinectis a relatively new device from which we can extract thedepth values and color values of an arbitrary position in theimage. Therefore, the Kinect becomes a powerful device tohelp robots to explore objects in 3D real world space. Inthis paper, we describe the way to combine color and depthimages from the Kinect with navigation data to build a realtime system of face detection. This combination gives us someadvantages. First, we can compute 3D geometric constraintsof objects based on depth values from the Kinect, which areused for removing non-face regions. Second, we can apply anew technique of depth-based skin segmentation for improvingthe efficiency of finding human skin as well as increasing thespeed of detecting faces. By combining depth values, we canisolate skin areas in different objects and in different distances.Furthermore, we can determine the distance from them tothe Kinect camera; and therefore, we can limit the size ofpotential faces in every skin region to reduce processing time.Third, the combination of depth values and navigation datagives robots an opportunity to determine 3D coordinates ofevery position in real world space. Thus, robots can easilyignore background regions, and only focus on the potentialfacial regions. We tested our method and achieved remarkable

Fig. 1. Our face detection is able to run in real time on a robot platformin an indoor environment. By using geometric constraints, navigation anddepth-based skin segmentation, the average processing time can be reducedby a factor of 50, and false positives are decreased by several tens of times

results of computational speed and accuracy. Figure 1 showsan example of our face detection which runs on the mobilerobot platform SCITOS G5.

The remainder of this paper is organized as follows: Wepresent previous researches on face detection in Section II. InSection III, our method is described in detail. In Section V,the experimental results obtained from our database are shownand conclusions are given in Section VI.

II. RELATED WORK

There is a long history of researching face detection inimages and in videos. Some of these approaches can be appliedto detect faces in real time, such as using skin color [8], depthinformation [11] and machine learning techniques [10], [9].

Especially, in applications for mobile robots, range datais used to make face detection faster. Blanco [3] utilizedlaser range scanners to determine potential candidates beforeapplying face detection methods. Fritsch [7] used a stereomicrophone to localize the voice of a talking person and detectthe persons legs using a laser range finder. Such methods thatuse 2D laser range scanners do not give highly accurate resultsbecause lasers provide poor features that let robots confusehuman legs with table legs or chair legs.

Additionally, geometric constraints based on depth informa-tion or lasers are also used to limit the search of facial regions,which was proved in [6]. Their approach uses geometric

constraints on possible human height and human facial sizeand can remarkably reduce the amount of average computationas well as decrease a large number of false positives. Eventhough this approach can reduce the computational cost by 85percent, it has not yet met the real-time requirements for animage resolution of 640×480.

One further approach of face detection using depth infor-mation is reported in [4]. Their method uses depth data froma stereo camera to calculate the corresponding size of faces,applies distance thresholding to avoid detecting faces in theareas that are too far from the camera with too few facepixels. In addition, they suppose that with traditional stereocameras, which provide sparse information, it is impossibleto calculate depth values in the areas without texture, andin such areas, face detection methods do not work. There-fore, the locations that do not contain depth information areunnecessary to detect. In addition, the combination of depthvalues and context information helps a robot to avoid findingfaces in irrelevant locations, such as ceiling, floor plane, etc.This method improved the processing time of face detectionsignificantly . It is able to decrease the computational cost by80 percent, but it reduces detection rate in the case that facesare far from the camera or the faces are close to the imagebolder.

III. APPROACH

In this section, we describe our approach of real time facedetection in detail. Our approach includes six steps: First, weuse a strategy of sampling to reduce computational costs whilenot affecting the accuracy of the algorithm. Instead of scanningthe whole image, we only collect data from several hundredsampling points that span the whole image. In the second step,we evaluate these sampling points under geometric constraintsto select appropriate points for finding potential face regions.Similar to the second step, in our third step, we use constraintsof navigation to extract the sampling points that belong toforeground and remove those which belong to the background.In the fourth step, skin detection is applied to the regions thatare around the filtered sampling points. If the density of theskin pixels around a sampling point is over a given threshold,this sampling point will be kept for the next step. In the fifthstep, all selected sampling points are used for depth-basedskin segmentation to isolate potential face regions as well asto estimate the sizes of the faces that possibly occur in theseregions. In the last step, these regions are scanned by a facedetector to localize human faces.A. Sampling

A popular and efficient technique that we applied in thispaper is a sampling technique. For not losing the informationof potential face areas, the sampling interval must be smallenough to detect the smallest faces, which in our experimentshave the size of 20×20 pixels . We choose a sampling intervalof 10 pixels in both horizontal and vertical directions asdescribed in Figure 2. In addition, the sampling positions inthe color image and in the depth image must have the samecoordinates. In every sampling point, the information set of

depth value, skin region and navigation data is collected andprocessed to serve the next preprocessing steps.

B. Geometric constraintsBased on the combination of color and depth images from

the Kinect and knowledge of the camera’s geometry, we cancompute 3D coordinates of every point. The robot can nowestimate the appropriate size of a human face at a certainsampling point. A sampling point which is too far from thecamera can be ignored because the robot can not detect anyfaces there even if they occur. The robot is also able toignore irrelevant regions which do not contain human faces forinstance, floor and ceiling. Furthermore, we limit the height ofthe search area because we know the minimum and maximumheight of humans when they sit on a chair, stand or walk. Wenow describe how to apply geometric constraints for a mobilerobot to limit the search space by using depth values collectedat sampling points:

First, we present the way to compute the transformationbetween coordinates of the robot frame and 2D coordinatesof the images. We denote the 3D coordinates of an arbitrarypoint in the robot frame with respect to the Kinect camerawith (cxp,

c yp,c zp); ixp is the depth value of this point in the

depth image; iyp and izp are 2D coordinates of this point in thecolor image. We mount the Kinect camera on the robot suchthat the normal vector of the depth image plane is parallel tothe cxp axis and cyp and czp axes are parallel to iyp and izpaxes of depth image, respectively. We can now compute the(cxp,

c yp,c zp) coordinates

cxp =ixp (1)

cyp =(iyo− iyp)

ixp

fy(2)

czp =(izo− izp)

ixp

fz(3)

where iyo and izo are 2D coordinates of the principal point,fy and fz are the focus lengths of depth camera. Now theheight in robot frame coordinates of sampling points in thedepth image can be computed. The sampling points which areout of range from minimum height hmin to maximum heighthmax are ignored since they are unlikely in potential facialareas. Furthermore, the sampling points that are too far fromthe camera are also ignored, as mentioned above

In addition, the robot can estimate the appropriate size ofhuman faces in different distances; therefore, it can limit therange of scales to detect faces. We assume that average size ofa human face is 0.15 meters; therefore, the formula to estimatethe size of faces in images is

s f ace =0.15 fy

d(4)

where s f ace is the average size of faces in the distance of dmeters. The constraint of face size contributes to a significantreduction of processing time because the robot only has to scanpotential facial areas in one scale instead of multiple scales.

Fig. 2. Example of sampling. The sampling interval is 10 pixels in bothhorizontal and vertical directions

C. NavigationOne of the advantages of current robot software is that

it contains different modules, including cameras and othersensors, motion control, navigation, etc. These modules runconcurrently and are able to share data. In our face detectiontask, we utilize the navigation module for reducing the spaceof searching faces. We found that the Kinect camera allowsa robot to determine 3D coordinates of every object withrespect to robot coordinates, while the navigation moduleprovides a robot with its 3D coordinates with respect to worldcoordinates. Therefore, we can track human activity around therobot. The fusion of these modules gives a robot the abilityto map the points in Kinect images to cells of an occupancygrid map. This mapping helps the robot to avoid searching thebackground regions whose corresponding cells are occupied inthe grid map, and focus on the regions of human activities,whose corresponding cells are free.

We note that (rxc,r yc,

r zc) are 3D coordinates of the Kinectcamera with respect to robot coordinates, (wxr,

w yr,w zr) are

the coordinates of the robot with respect to the origin of thenavigation map and θ is the rotating angle of the robot. Thus,the coordinates of an arbitrary point in the world space withrespect to robot coordinates, (rxp,

r yp,r zp), are computed asrxp

ryprzp

=

rxcrycrzc

+

cxpcypczp

(5)

After that the coordinates of this point with respect to theorigin of the grid map, (wxp,

w yp,w zp), are computed by the

following formulas:wxpwypwzp

=

cosθ −sinθ 0sinθ cosθ 0

0 0 1

rxpryprzp

+

wxrwyrwzr

(6)

These coordinates are mapped to an occupancy grid mapto determine the corresponding cell. As a result, we use theabove formulas for updating navigation information for everysampling point. A sampling point would not be used forsegmenting potential face regions when it is mapped to anoccupied cell in the grid map because it belongs to a certainbackground area.

D. Skin detectionIn this section, we explain a skin detection technique

mentioned in [8]. In general, the idea of this method is that

it tries to eliminate the influence of non-standard illuminationbefore using a set of simple rules which are very efficient toclassify skin and non-skin pixels under standard illuminationconditions. In order quickly eliminate non-standard illumi-nation from images we use the Gray World assumption ofcolor compensation. The Gray World theory is built on thepresumption that the average surface color on the image isachromatic. With this presumption, we try to find the scalefactors which adjust the color channels R, G and B in such away that their mean values are equal to the ones under standardillumination. The scale factors are calculated as follow

sn =tnan

(7)

where sn is the scale factor of channel n, tn is the standardgray value of channel n, and an is the average value of channeln. Because the computation of the gray world method inthe whole image is a quite expensive while the illuminationdoes not change too much in short time, the scale factors areonly adjusted every second. After eliminating non-standardillumination, image pixels are classified as skin or non-skinusing a set of fast and simple rules. So we denote I(i, j) asthe skin value in a point at row i and column j in the colorimage. I(i, j) is equal to 1 if following rules [8] are satisfiedand equal to 0 if not:

R > 95 AND G > 40 AND B > 20 AND

Max{R, G, B}−Min{R, G, B}> 15 AND

|R−G|> 15 AND R > G AND R > B

where R,G,B are three values of red, green, and blueelements in an arbitrary pixel, respectively. Such skin detectionmethod will be applied to determine whether the area arounda sampling point in color image is skin or not. T (m,n) isdefined as the sum of skin values in a set of pixels around thesampling point at coordinates (m,n) and is computed by thefollowing formula:

T (m,n) =2

∑i=−2

2

∑j=−2

I(sm+2i,sn+2 j) (8)

where s is the sampling interval. The area around thesampling point at coordinates (m,n) is a skin area if T (m,n)is higher than a threshold, otherwise it is non-skin area. Thecalculation of skin areas around sampling points improves theskin segmentation that will be mentioned in the next session.

E. Depth-based skin segmentationColor-based skin segmentation is known to be a fast and

efficient method of finding potential facial regions. However,a drawback of this method is that it is much influenced bydifferent illumination sources and also detects skin-tone colorobjects (wood, hair, some clothes...), which both cause thesegmentation to fail. In many cases, segmented areas wouldoccupy a big part of the image. It makes the face detectionsystem run very slowly and less accurate. Furthermore, theprevious method of skin segmentation is used for classifying

Fig. 3. Result of depth-based skin segmentation. The skin regions such asface, hand are segmented while false positives in background are removed byconstraints of geometry and navigation

every pixel into differently labelled skin regions, which takesa lot of time. In this section, we describe our technique ofdepth-based skin segmentation which improves efficiency offinding human skin as well as reduces the computationalcost. Instead of classifying every pixel, we try to label everysampling point in such a way that two sampling points havethe same label if their sets of conditions of depth values,geometric constraints, navigation constraints, and skin valuesare similar. The technique of skin segmentation using samplingpoints remarkably reduces the processing time, in our caseby a factor of 100. The number of sampling points wouldbe reduced further if we use constraints of geometry andnavigation together with skin detection to filter samplingpoints out of interest ranges. After using such constraints,unnecessary sampling points are removed, and we use a fastand accurate algorithm to segment these filtered samplingpoints. This algorithm is used to classify filtered samplingpoints into different labelled regions instead of classifyingimage pixels. In our algorithm, two filtered sampling points Aand B will have the same label if the distance between themis under a threshold, which is set to a half of the average sizeof a face estimated at the sampling points. This threshold isapplied to remove the effect of disconnected skin regions ona face which are made by noise and concave areas aroundeyes. With such a strategy, all filtered sampling points areclassified rapidly into different skin regions as described inFigure 3. In this figure, the false positives in the backgroundare removed by using constraints of geometry and navigation,and the depth-based skin segmentation is applied to isolatepotential face regions.

The algorithm of depth-based skin segmentation is shownto be faster and more accurate than previous algorithms ofconnected components since we can remove false positivesin background and eliminate affect of noise without usingexpensive operators of erosion and dilation

F. Face detection

In our system, we use the Viola-Jones algorithm [10] forour system of face detection because it can process imagesextremely quickly and is very accurate. There are three keycontributions that result in the extremely efficient Viola-Jones

algorithm: the integral image, Adaboost learning, and thecascade architecture. The integral image is an effective imagerepresentation to reduce computational costs when testing ortraining Haar features. Adaboost learning allows to train a fastand accurate classifier by selecting a small number of the bestfeatures from tens of thousands of possible features. Finally,these classifiers are combined to make a cascade architecturemodel to quickly reject a large number of false positives inthe stages until achieving a high detection rate. Based onthese contributions, the Viola-Jones method is likely the fastestmachine learning method for face detection. However, whendealing with a high resolution image, for instance, 640×480in our system, the Viola-Jones method alone does not meetthe requirement of a real time face detection system formobile robots. Therefore, in our system, we apply the abovepreprocessing steps to reduce the computational cost beforeusing the Viola-Jones face detector.

IV. EXPERIMENTAL SETUP

In this section, we present the experimental evaluation of oursystem by using different constraints. To evaluate the accuracyas well as the speed of our algorithm, we compared it with theperformance of Viola-Jones algorithm of face detection builtin the Intel OpenCV library [1]. All experiments are basedon our database collected from two kinds of mobile robots,PR2 and SCITOS G5, and from different indoor environments,including offices, corridors, kitchen, museum and laboratory.The robots are equipped with Kinect sensors and Sick S300laser scanners. We use a PC with a 2.4 GHz Intel Core 2 CPUto test our algorithms in these experiments.

A. DatasetsTo evaluate our face detection algorithm, we used two

challenge datasets. The first one is the Michigan dataset whichcomprises seven ROS log files spanning from one to threeminutes, which are selected from datasets of detecting andtracking humans [5]. The selected log files must contain frontfaces in color images in different indoor environments, andin different illuminations conditions. These log files in theMichigan dataset recorded only Kinect color and depth imagesat 30 frames per second, but do not contain an occupancy gridmap; therefore, we just use it for the first experiment whichdoes not require constraints of navigation. All these log filesare collected from the a Willow Garage mobile robot PR2that moves around an office building with kitchens, corridors,meeting rooms and departments to detect human front faces.The mobile robot PR2 is equipped with a Kinect camera that ismounted 2.0 meters high on the top of robot’s head, and looksdownward. The second dataset is the Tuebingen dataset whichconsists of seven ROS log files collected from a MetraLabsmobile robot SCITOS G5. Every log file lasts from one minuteto three minutes, and contains Kinect color and depth images,tf transform messages, and laser range data. This mobile robotused a Kinect camera mounted 1.1 meters high and lookingforward. To record the log files, the mobile robot had tomove around in our office building, including the laboratory,the corridors and the museum with different backgrounds,

and different illumination conditions. Moreover, we providedan occupancy grid map of our building for mobile robots;therefore, the Tuebingen dataset can be used for both of theexperiments to test face detectors with or without navigation.B. Implementation Details

To evaluate the efficiency of the constraints in improvingthe processing time and accuracy, we carried out two differentexperiments, in which we compared our algorithm with theOpenCV face detector. The OpenCV face detector was runfrom a smallest size of 20×20 pixels and scaled up by a factor1.2 in whole images. The first experiment was implemented toevaluate the role of geometric constraints and depth-based skinsegmentation in improving the face detection performance. Aface detector using geometric constraints, depth-based skinsegmentation but not navigation is shown to can run efficientlyin real time on mobile robot platforms as well as in surveil-lance systems. This detector was compared with the OpenCVface detector in accuracy and processing time. In the secondexperiment, a second face detector using geometric constraints,navigation and depth-based skin segmentation was comparedwith the above face detector without using navigation andthe OpenCV face detector. This experiment was performedto demonstrate the efficiency of navigation information inreducing the number of false positives and the computationalcost.

Our whole system is programmed based on the ROS system[2]. ROS is a powerful system for software developers whichprovides libraries, tools, message-passing, package manage-ment for building robot applications. Our robot system consistsof many ROS nodes such as the node of controlling the laserrange-finder, the node of performing localization, the nodeof managing the Kinect operation, the node of providing thegraphical view, and the node of process odometry information.By using ROS, the color and depth images from the Kinectcan be synchronized and fused with other sensors, and robotcoordinates are also updated frequently based on the tf packagewhich is responsible for computing 3D coordinate frames.Furthermore, an occupancy grid map of our building is usedto provide the information of background for mobile robots.

Our face detection system can run at 7.9 milliseconds perframe on average with an accuracy of over 95 % in our datasets

V. RESULTSIn this section, we compare the results of the accuracy and

speed between our face detection algorithm and the OpenCVface detector. The first experiment of testing the efficiencyof geometric constraints and depth-based skin segmentationwas implemented in both datasets. The second experiment fortesting the influence of navigation ran on the Tuebingen datasetwhich includes the occupancy grid map.

A. Processing timeTable I shows a distinguished difference between our de-

tector and the OpenCV face detector in processing time whenthey are run on both datasets. We denote the first methodas the face detection method using geometric constraints anddepth-based skin segmentation.

TABLE ICOMPARISON OF PROCESSING TIME BETWEEN THE OPENCV FACE

DETECTOR AND THE FIRST METHOD.

Datasets OpenCV First Method

Michigan 500.1 ms 8.7 msTuebingen 530.9 ms 13.1 ms

TABLE IICOMPARISON OF PROCESSING TIME AMONG THE OPENCV FACE

DETECTOR, THE FIRST METHOD AND THE SECOND METHOD.

Dataset OpenCV First Method Second Method

Tuebingen 530.9 ms 13.1 ms 7.9 ms

We can find that processing time of the face detector usinggeometric constraints and depth-based skin segmentation ismuch less than that of the OpenCV face detector because itsaverage processing time is only 8.7 ms in the Michigan dataset,and 13.1 ms in the Tuebingen dataset. At such speed, it runs asmuch as 41 to 57 times faster than the OpenCV face detector.It also means that the use of geometric constraints and depth-based skin segmentation can reduce the computational cost by98 %.

To evaluate the contribution of navigation, we continuedimplementing the second experiment to compare the facedetector using geometric constraints, navigation and depth-based skin segmentation with two above detectors. Table IIshows the results of the three methods which are run in theTuebingen dataset. We denote the second method as the facedetection method using geometric constraints, navigation anddepth-based skin segmentation.

With average processing time of 7.9 ms, the second methodis even faster than the first method, and it also means thatthe computational cost are reduced by 99 %. Therefore, thenavigation information plays an important role in avoidingsearching irrelevant regions as well as avoiding unnecessarycomputations. That is one of the advantages of mobile robotin building such real time systems as face detection, or facerecognition.

B. AccuracyTo evaluate the accuracy of a face detection method, we

have to consider both the ratio of true positives and the ratio offalse positives detected in the datasets. A good algorithm mustnot only achieve a high detection rate of true positives but alsolimit false positives in an image. In the experiments, we foundthat besides the advantage of processing speed, our methodscan also improve the accuracy of face detection significantly:the ratio of detected true positives is similar to the OpenCVface detector, the ratio of false positives is much lower.

In the first experiment, we use both datasets without theoccupancy grid map for testing the OpenCV face detectorand the first method. Table III shows the comparison of theaccuracy between these face detectors. The correct detectionrate of our method is still high, 95 %, even though it is a littlelower than the OpenCV face detector, 96 %. But our methodimproved remarkably the average false positives per frame,only 0.003, compared to the OpenCV face detector with 21times more, 0.063. In general, the accuracy of our method isstill more reliable than the unmodified OpenCV face detector.

TABLE IIICOMPARISON OF THE ACCURACY BETWEEN THE OPENCV FACE

DETECTOR AND THE FIRST METHOD.

FaceDetectors

Correct DetectionRate (%)

Average FalsePositives per Frame

OpenCV 96 0.063First Method 95 0.003

TABLE IVCOMPARISON OF THE ACCURACY AMONG THE OPENCV FACE DETECTOR,

THE FIRST METHOD AND THE SECOND METHOD.

Face Detectors Correct DetectionRate (%)

Average FalsePositives per Frame

OpenCV 97 0.079First Method 95 0.005

Second Method 95 0.005

In the second experiment, we only use the Tuebingen datasetwhich contains the occupancy grid map for testing three facedetectors. Table IV shows the comparison of the accuracyamong them. We also compared the face detectors based ontwo criteria of the correct detection rate and the average falsepositives per frame. Both of the first and second methodsachieve the correct detection rate of 95 %, which is slightlylower than the OpenCV face detector but their average falsepositives per frame are 16 times less than the OpenCV facedetector. This showed that our face detectors are reliable formobile robots. The reason is that the preprocessing stepseliminate almost all of the background which contains a lotof false positives, but do not affect the correct detection ratemuch.

Figure 4 shows the result of our face detection in differentcases: a group of people standing at roughly the same distance;several people standing at different distances to the robot;people standing near a wall; people with different skin color;people at far distances.

VI. CONCLUSION

This paper demonstrates that the contributions of geomet-ric constraints, navigation information and depth-based skinsegmentation to face detection are remarkable. Based on theircombination, mobile robots are able to localize human facesin real time, and interact with humans in a more reliableway. In our future work, we focus on a promising directionof face tracking using navigation information. When humanmoving directions are repetitive, a robot can compute priorprobabilities to predict the next face positions in 3D space.

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