5 インチ PDP カメラ (a) (b) 1 Fig. 1 Information display. (a) f=25mm (b) f=16mm 2 UXGA Fig. 2 Examples of captured image. [3] [4] 1 [5] [7] 1 3pixel 5 1 7pi

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1 THE INSTITUTE OF ELECTRONICS INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE {j-satakeakihiro-k}@nict.go.jp {hirayamakawashimatm}@i.kyoto-u.ac.jp UXGA 3fps Abstract Accuracy Improvement of Real-Time Gaze Estimation using High Resolution Camera Junji SATAKE Akihiro KOBAYASHI Takatsugu HIRAYAMA Hiroaki KAWASHIMA and Takashi MATSUYAMA National Institute of Information and Communication Technology 3 5 Hikaridai Seika-cgo Soraku-gun Kyoto Japan Kyoto University Yoshida-Honmachi Sakyo-ku Kyoto Japan {j-satakeakihiro-k}@nict.go.jp {hirayamakawashimatm}@i.kyoto-u.ac.jp We develop an interactive information display that estimates face and gaze directions of a user in real-time and interactively controls contents by recognizing the user s interest and reaction. The gaze estimation method that uses only the camera image generally estimates the gaze direction as a line that connects the center and the eyeball center. However because the resolution of eye area was low the influence of error was large. We report an accuracy improvement of real-time gaze estimation using high resolution camera and detailed model. Key words gaze estimation high resolution camera detection 1. [1] [2]

5 インチ PDP カメラ (a) (b) 1 Fig. 1 Information display.

5% 2. 2 1(a) 1(b) Point GreyResearch Grasshopper UXGA

8 CCD IEEE-1394b FUJINON HF25HA-1B f=25mm HF16HA-1B

2 5 インチ PDP カメラ (a) (b) 1 Fig. 1 Information display. (a) f=25mm (b) f=16mm 2 UXGA Fig. 2 Examples of captured image. [3] [4] 1 [5] [7] 1 3pixel 5 1 7pixel [8] LmedS [9] 5% (a) 1(b) Point GreyResearch Grasshopper UXGA (16 12) 8bit 3fps 1 / 1.8 CCD IEEE-1394b FUJINON HF25HA-1B f=25mm HF16HA-1B f=16mm 2 6pixel 3 Fig. 3 Expansion image of eye part. RIFA-F 5 5cm 3 6pixel 2pixel [3] [3] [1] [11] 4 mm.2.6mm.2mm 11mm 2

23 24mm 13mm 1 5 α l l = c c 2 r 2 sin α r = 5.5mm c = 5.8mm d = 13mm 3. 4 α α < arccos r c 18 3. 3. 1 6 1m Z w X w Y w Z c [ ] T [ λ u v 1 = P X w Y w Z w 1 P OpenCV 3.

5 眼球 ( 直径 23) 眼球中心 ( 旋回中心 ) 中心窩耳側 Structural model of eyeball. 旋回中心 (a) Fig. 6 黒く見える領域 5 Fig. 5 3. 3 光軸厚み l 図 (c) の視点方向虹彩中心 (b) 接点厚み l 虹彩中心曲率中心 (c) α Calculation of outline.

3 23 24mm 13mm 1 5 α l l = c c 2 r 2 sin α r = 5.5mm c = 5.8mm d = 13mm 3. 4 α α < arccos r c m Z w X w Y w Z c [ ] T [ λ u v 1 = P X w Y w Z w 1 P OpenCV 3. 2 Active Appearance Model(AAM) [12] [13] AAM 45 7 ] T 固視点視軸光軸黒く見える領域カメラ曲率半径 c 虹彩中心 Fig. 4 虹彩半径 r d 左目を上から見た図角膜角膜による屈折虹彩 ( 直径 11) 4 強膜角膜曲率中心水晶体鼻側 11.5 眼球 ( 直径 23) 眼球中心 ( 旋回中心 ) 中心窩耳側 Structural model of eyeball. 旋回中心 (a) Fig. 6 黒く見える領域 5 Fig 光軸厚み l 図 (c) の視点方向虹彩中心 (b) 接点厚み l 虹彩中心曲率中心 (c) α Calculation of outline. カメラ座標系モデル座標系 6 Xc Zc Xw Zw Yw ワールド座標系 r c 画像座標系 u v Yc Xm Zm Ym Definition of each coordinate system [ ] T = ϕ θ ψ t x t y t z i (X mi Y mi Z mi ) (X wi Y wi Z wi ) X wi Y wi Z wi = R z(ϕ)r y(θ)r x(ψ) X mi Y mi Z mi + t x t y t z 3

8 3D face shape model. 4 Ym [mm] (b) (c) (d) 9 Fig. 9 Iris detection.

4 Fig. 7 7 AMM Example of Active Appearance Model. feature point direction of face turn center of eye Zm [mm] feature point direction of face turn center of eye (a) -4 4 Zm [mm] Xm [mm] Ym [mm] Xm [mm] 8 3 Fig. 8 3D face shape model. 4 Ym [mm] (b) (c) (d) 9 Fig. 9 Iris detection. f AAM (u i v i ) (u i ( ) v i ( )) k+1 = k a f ( k) f( ) = w i {ui ( ) u i } 2 + {v i ( ) v i } 2 i a w i AAM (a) 5 4 w i = d 13mm (a) (b) [14] 9(c) (d) ( X Fig. 1 Y Z ( u ) v 1 ) d ( X center Y center Z center Estimation of center and gaze. 1.2 I src I temp E ( 1 < = E < = 1) 1 1 I src = 1 I temp = 1 E (u v) = 3. 5 Isrc (u + u v + v ) I temp (u v ) {Itemp (u v )} 2 3 (X center Y center Z center ) 3. 3 d (u v ) 3 (X Y Z ) 1 X u Y λ v = P Z 1 1 X Y Z ) X center Y center Z center = d 2 (X Y Z ) 4

X gaze Y gaze X gaze Y gaze Z gaze X center Y center Z center = λ X Y Z X center Y center Z

4. 1 E Y gaze E 2cm 15 11 1m 12 12 7 8 9 2.

下カメラ (a) (b) 11 Fig. 11 Experimental environment. 7 8 9 (a) (b) 12 Fig. 12 Results of detection.

5 X gaze Y gaze X gaze Y gaze Z gaze X center Y center Z center = λ X Y Z X center Y center Z center 1m Z gaze = (X gaze Y gaze ) E E >.4 X gaze = E X gaze E Y gaze = E Y gaze E 2cm m pixel 33mm α α > 3 UXGA 左カメラ右カメラ下カメラ (a) (b) 11 Fig. 11 Experimental environment (a) (b) 12 Fig. 12 Results of detection. 15mm/pixel E XGA mm UXGA XGA 25mm UXGA mm 3 1mm 4. 2 PC Intel Core2 Duo 2.66GHz 2GB 1 PC 2 Intel Integrated Performance Primitives OpenCV cvremap 5

6 1 [mm] 5 Table 1 Error of gaze estimation. UXGA XGA UXGA 3fps 1ms 5. UXGA 3 3 3fps No [1] HCI no.99 pp [2] Mindprobing: HCI no.99 pp [3] 2 vol.44 no.4 pp Table 2 1 Time of processing a frame. UXGA XGA.8ms.5ms 11ms 4.5ms 15ms 1ms 3ms 3ms 1.3ms 1ms (1ms) (7ms) 31ms 19ms [4] C. Hennessey B. Noureddin and P. Lawrence A Single Camera Eye-Gaze Tracking System with Free Head Motion ETRA pp [5] R. Newman Y. Matsumoto S. Rougeaux and A. Zelinsky Real-Time Stereo Tracking for Head Pose and Gaze Estimation FG pp [6] CVIM vol.47 no.sig15 pp [7] MIRU pp [8] J.G. Wang E. Sung and R. Venkateswarlu Estimating the eye gaze from one eye CVIU vol.98 pp [9] LMedS MIRU pp.i [1] O plus E vol.22 no.4 pp [11] [12] T.F. Cootes G.J. Edwards and C.J. Taylor Active appearance models PAMI vol.23 no.6 pp [13] M.B. Stegmann B.K. Ersbøll R. Larsen FAME - A Flexible Appearance Modeling Environment IEEE Trans. Medical Imaging vol.22 no.1 pp [14] (D) vol.63 no.4 pp

258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System

258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.