[2], [3], [8], [20] [4] [6], [18] [1], [11], [19] [13] [10] N SVD PCA N SVD Vasilescu Vasilescu N SVD [14] [17] Y Li [7] Y Li N SVD [12] 2,,,,, 596

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "[2], [3], [8], [20] [4] [6], [18] [1], [11], [19] [13] [10] N SVD PCA N SVD Vasilescu Vasilescu N SVD [14] [17] Y Li [7] Y Li N SVD [12] 2,,,,, 596"

Σαβαώθ Παπανδρέου
5 χρόνια πριν
Προβολές:

1 画像の認識理解シンポジウム (MIRU2006) 2006 年 7 月 y y y y yfonoy,takahiro,ysatog@iisu-tokyoacjp N SVD Gaze Estimation from Low Resolution Images Consideration of Appearance Variations due to Identity by Using Kernel-based Multilinear Models Yasuhiro ONO y, Takahiro OKABE y, and Yoichi SATO y y Institute ofindustrial Science, The University oftokyo Komaba, Meguro-ku, Tokyo, JAPAN yfonoy,takahiro,ysatog@iisu-tokyoacjp Abstract We propose an appearance-based method for estimating gaze directions from low resolution images Positioning errors of eye regions and appearance variations due to changes in identity may seriously degrade the accuracy of gaze estimation To improve the accuracy, we consider a nonlinear relationship between eye images and the feature vectors for each mode, for example, gaze direction and identity Thus, we introduce the use of kernel-based multilinear models We prepare a set of training images with various gaze directions and positioning errors from a few people The training images are mapped into a high dimensional space via a nonlinear mapping In the space, feature vectors for gaze direction are extracted We describe our method and report experimental results demonstrating the merit of our method Key words gaze estimation,low resolution,appearance-based method,identity,nonlinear N-mode SVD 1 595

2 [2], [3], [8], [20] [4] [6], [18] [1], [11], [19] [13] [10] N SVD PCA N SVD Vasilescu Vasilescu N SVD [14] [17] Y Li [7] Y Li N SVD [12] 2,,,,, 596

3 2 1 N SVD N SVD [14] N SVD N,, 4 SVD i(1< = i< = I) j(1< = j< = J) k(1< = k< = K) l(1< = l< = L) 4 Z2< I J K L (Z) ijkl N SVD Z Z = ^D 1 ÛG 2 ÛPOS 3 ÛP ^D ÛG, ÛPOS, ÛP 2 2 N SVD N SVD Y Li N SVD [7] N SVD Z F Φ Φ(Z):= W2< I J K M (M > L) W W = D 1 UG 2 UPOS 3 UP F UG, UPOS, UP D2< I J K M D = W 1 U T G 2 U T POS 3 U T P (1) 4 W W (G) = Φ(z 111 ) T ::: Φ(z 11K ) T ::: Φ(z 1J1 ) T ::: Φ(z 1JK ) T Φ(z 211 ) T ::: Φ(z 21K ) T ::: Φ(z 2J1 ) T ::: Φ(z 2JK ) T Φ(z I11 ) T ::: Φ(z I1K ) T ::: Φ(z IJ1 ) T ::: Φ(z IJK ) T z ijk 2< L i j k Φ(z ijk )2< M Φ z ijk F BG2< I I BG : = (W (G) )(W (G) ) T T T = (UG± GVG )(UG± GVG ) T = UG± 2 G U G T 2< I I (2) Φ BG JX (B G)m;n = KX Φ(z mjk ) Φ(z njk )= JX KX k(z mjk ; z njk ) j=1 k=1 j=1 k=1 (2) (3) UG N SVD UG i(1< = i< = I) u i G UG T =[u 1 G; u 2 G; :::; u I G] UPOS UP 2 3 (1) (2) (1) PC (2) (2) (3) UG k(z mjk ; z npq )=exp kz mjk z npq k =c 2 2< 2 4 (1), (2), (3), (1), (2) [12] Z test 2< L F Φ(Z test )2< M N SVD Φ(Z test ) P (G) m = I P J P K i=1 j=1 k=1 D ijkm(u G)i(uPOS)j(uP )k Φ(Z test ) (G) 2< 1 M 4 Φ(Z test )2< M A (n) 2< In (I 1I 2 :::I n1i n+1 :::I N ) N (3) 597

4 A2< I 1 I 2 ::: I N n ug2< I, upos2< J, up 2< K F f(ug; upos; up ) f(ug; upos; up ):= ψ Φ(Z test ) (G) m MX m=1 IX (ug; upos; up )= JX KX i=1 j=1 k=1 arg minu G 2< I ;u POS 2< J ;u P 2< K f(ug; upos; up ) (ug; upos; up ) (4) ug, upos, up (4) f G)i =0 (1 < = i< = I) ; POS)j =0 (1 < = j< = J) ; P )k =0 (1< = k< = K) : (7) UG, UPOS, UP g, o, p (TG) (G) Φ(z test )2< I Φ (T G)(G) (T G)(G) = P T i;j J P K P J P K onpmo n=1 m=1 l=1 k=1 lp k k(z inm ; z jlk ), (TG)(G) Φ(z test ) i = P J j=1 P K k=1 ojp kk(z ijk ; z test )! 2 Dijkm(u G)i(uPOS)j(uP )k (4) (3), i (1)= arg min i2f1;2;:::;ig kug u i Gk 2 u i(1) G i(1) 2 3 u i(2) G, ui(3) G, P 3 ffl = kug p=1 wpui(p) P G k2 3 p=1 wp =1 0 < = w p < = 1(p =1; 2; 3) P 3 g = wpg(p) p=1 g g(p)(1< = p< = 3) u i(p) G 3 2 g := UGuG; o := UPOSuPOS; p := UP up (8) 4 T G2< I 1 1 M T G := W 2 o T 3 p T (9) z test 2< L Φ(Z test ) (G) Φ(Z test ) (G) = Φ z test T 2< 1 M (10) (1), (4), (8), (9), (10) (5) ug ug = U T G T + test (TG) (G) Φ(z ) = UG T T 1 (TG) (G) (TG) (G) (TG)(G) Φ(z test ) (11) (TG) (G) 4 T G M + + T I I (11) (TG) (G) (TG) (G) 2< 3 1,, [9], IEEE1394 (Point Grey Research Flea) 3 1, , cm, 598

5 1 LCD Fig 1 Alayout of targets displayed on the surface on an LCD monitor: circles for training and crosses for test 2 (a), (b),(c),(d) Fig 2 (a)candidates of eye corners (b),(c),(d)a schematic diagram of segmented eye images 3 2 (1) (2) (3) (1), (2) (1) 1 1, 20, (2), ( ), ( ) 36 36, [9] ( 2 ) 1 ( 2 ), 2 (b),(c),(d) 12 4=48, 25,,,, 3 :(a), (b) Fig 3 Examples of training images for (a)different gaze points and (b)different segmentations, = = 800, 3 3(a),,, 3(b) 3 3 N SVD N SVD (1) 0 9 4( ) 4( ) ( ) 4 (2)

6 4 ( ) ( ) 5 Fig 4 Gaze estimation error for each individual(left), gaze estimation error averaged by all individuals(right) Fig 5 Gaze estimation error for each segmentation, [1] Baluja, S and Pomerleau, D: Non-intrusive gaze tracking using artificial neural networks, CMU CS Technical Report, CMUCS94102 (1994) [2] Beymer, D and Flickner, M: Eye GazeTracking Using an Active Stereo Head, In Proc IEEE CVPR 2003, pp II (2003) [3] Hutchinson, T, White, K, JR, Martin, W, Reichert, K and Frey, L: Human-Computer Interaction Using Eye-Gaze Input, IEEE Trans SMAC, Vol 19, No 6, pp (1989) [4] Ishikawa, T, Baker, S, Matthews, I and Kanade, T: Passive Driver Gaze Tracking with Active Appearance Models, In Proc WCITS 2004 (2004) [5] ConDensation,, CVIM , pp 1724 (2005) [6] Matsumoto, Y and Zelinsky, A: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement, In Proc IEEE FG 2000, pp (2000) [7] Li, Y, Du, Y and Lin, X: Kernel-Based Multifactor Analysis for Image Synthesis and Recognition, In Proc ICCV2005, pp (2005) [8] Ohno, T and Mukawa, N: A Free-head, Simple Calibration, Gaze Tracking System That Enables Gaze-Based Interaction, In Proc ACM ETRA 2004, pp (2004) [9] Oka, K, Sato, Y, Nakanishi, Y and Koike, H: Head pose estimation system based on particle filtering with adaptive diffusion control, In Proc IAPR MVA 2005, pp (2005) [10],,, (MIRU 2005), pp 96103, (2005) [11] Stiefelhagen, R, Yang, J and Waibel, A: Tracking Eyes and Monitoring Eye Gaze, In Proc WPUI, pp (1997) [12] Shum, H, Ikeuchi, K, and Reddy, R: Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling, IEEE Trans PAMI, Vol 17, No 9, pp (1995) [13] Tan, K, Kriegman, D and Ahuja, N: Appearance-based Eye Gaze Estimation, In Proc IEEE WACV, pp (2002) [14] Vasilescu,MAOand Terzopoulos, D: Multilinear Analysis of Image Ensembles: TensorFaces, In Proc ECCV 2002, pp (2002) [15] Vasilescu, M A O and Terzopoulos, D: Multilinear Image Analysis for Facial Recognition, In Proc IAPR ICPR 2002, pp II (2002) [16] Vasilescu, M A O: Human Motion Signatures: Analysis, Synthesis, Recognition, In Proc IAPR ICPR 2002, pp III (2002) [17] Vasilescu, M A O and Terzopoulos, D: TensorTextures: Multilinear Image-Based Rendering, In Proc ACM SIG- GRAGH 2004, Vol 23, No 3, pp (2004) [18] Wang, J, Sung, E and Venkteswarlu, R: Eye gaze Estimation from a Single Image of One Eye, In Proc IEEE ICCV 2003, pp I (2003) [19] Xu, L, Machin, D and Sheppard, P: A Novel Approach to Real-time Non-intrusive Gaze Finding, In British Machine Vision Conference, pp (1998) [20] Yoo, D and Chung, M: Non-intrusive Eye Gaze Estimation without Knowledge of Eye Pose, In Proc IEEE FG 2004, pp (2004) 600

Σχετικά έγγραφα

Gaze Estimation from Low Resolution Images Insensitive to Segmentation Error

Gaze Estimation from Low Resolution Images Insensitive to Segmentation Error (MIRU2005) 2005 7 153 8505 4 6 1 E-mail: {onoy,takahiro,ysato}@iis.u-tokyo.ac.jp appearance-based methods SVDSingular Value Decomposition N-mode SVD N-mode SVD, PCAPrincipal Component Analysis appearance-based