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ใॲཧձڀݚใ ࠂIPSJ SIG Technical Report ʑͳาߦঢ়گԼʹΔา༰ख๏ͷධՁ ౦ ါਅ 1,a) ᴳ ݪ༃ 1,b) ߃2,c) ീ ߁ 1,d) ཁɿɼࢦɼ࠼ͳͲʹଓ৽όΠΦϝτϦοΫͱɼηϯαΒΕॴ ͰຊਓՄͳา༰ʹߴ৺·ΔɽΕ·Ͱʹଟͷา༰ख๏ఏҊΕ ΔɼΕΒͷख๏ʹରΔʑͳঢ়گΛఆแతͳධՁະͳΕͳɽͰɼ ຊڀݚͰɼʑͳঢ়گԼʹɼา༰ख๏ͷධՁΛߦͱΛతͱΔɽධՁʹ༻ Δา༰ಛɼγϧΤοτʹجͷา༰ಛʹՃɼಈใΛΑΓۃతʹར༻า༰ಛ ͰΔϑϨʔϜʹجମߴہॴݾ૬ಛɼΦϓςΟΧϧϑϩʔʹج Gait motion descriptors Λར༻Δɽ·ɼಛͷর߹ख๏ͱɼϢʔΫϦουڑٴͼਖ਼४ผੳΛ༻Δɽ ݧͰɼมԽΔ߹ɼมԽΔ߹ͷೋͷঢ়ʹگΔา༰ख๏ͷධՁΛ ߦɽʹޙ࠷ɼಘΒΕՌΛߟɼา༰ख๏ͷ༗Λൺɽ Ωʔϫʔυɿา༰ಛɼධՁɼมԽɼมԽɼর߹ख๏ 1. Ίʹ ɼݸਓใอޢ๏ͷߦࢪతࡒͷՃʹΑΓɼ ݸਓγεςϜʹΔηΩϡϦςΟͷڧԽॏཁࢹ ΕΔɽ࠷Ͱɼԕ࡞ΟϧεʹΑΔͷ औΓɼѱͳϋοΩϯάʹΑΔݸਓใͷग़ͱ αΠόʔଟΓɼηΩϡϦςΟڴҖ ଞਓͰͳͱΖ·ͰഭΔɽͷΑͳഎ ܠͷதɼਓͷੜମใΛར༻όΠΦϝτϦοΫ ߴ৺ΛΊΔɽ όΠΦϝτϦοΫͱɼࢦٿ؟ͷ࠼ɼ੩຺ͱ ମతಛॺͱߦಈతಛʹΑຊਓ ΛߦͷͱͰɼੜମใวɼ། ҰɼӬଓͱಛΛར༻ΔͱΒɼ҉൪߸ ύεϫʔυʹൺݪཧతʹݻڧͳज़ͰΔͱߟ ΒΕΔɽΞϝϦΧ߹ࠃͷύεϙʔτίϯτϩʔϧʹ ɼࢦٴͼإըΛऔಘΔͱͰɼςϩϦετ ࢦख൜ͷೖࠃΛࢼΈͳΕΔɽΠϯυʹ ɼ12 ԯʹΔେͳͷࠃຽͷݸਓΛ Δख๏ͱɼࢦɼ࠼ɼإͳͲͷόΠΦ 1 େࡕେ Osaka University 2 υϨΫηϧେ Drexel University a) [email protected] b) [email protected] c) [email protected] d) [email protected] ϝτϦοΫΛར༻ΔϓϩδΣΫτ [1] ਐߦ ΔɽΕɼόΠΦϝτϦοΫͷརͰΔɼޡ ͷɼඃʹΔෛ୲ɼΠϯυͷΑ ͳ৽ʹࠃڵͱड༰ͱཧ༝ͱڍΒ ΕΔɽଞʹɼࢦʹΑΔܭػϞόΠϧͷϩάΠ ϯγεςϜɼ੩຺ʹΑΔΞΫηείϯτϩʔϧγεςϜɼ ࢦ DNA ʹΑΔ൜ࡑͷΊͷఆγεςϜͰར ༻ΕΔɽ·ɼຊࠃʹɼ ߦATM ʹ ɼ҉൪߸ʹՃࢦͷ੩຺ύλʔϯͷใΛར༻ ΔͱͰηΩϡϦςΟͷڧԽΛਤΔɽ ͳΒɼࢦɼٿ؟ͷ࠼ɼͱମత ಛΛར༻όΠτϝτϦΫεɼηϯαΒͷ ڑͳΕͳΒͳඃΒʹ ΘͳΕͳΒͳͱΔɽͷΑͳΛ όΠΦϝτϦοΫͱɼηϯαΒΕҐஔ ͰΛߦͱͰΔɼਓͷาͷݸʹجา ༰ΛΊΓɼ൜ΧϝϥΛར༻Ҭ ࢹ൜ࡑͷԠ༻ظΕΔɽʹࡍɼΠΪϦ εͰɼڧ౪൜ʹରΔา༰ͷՌࡋॴʹΔ ڌͱ࠾༻Ε [2] Γɼ·ɼຊʹ ɼา༰ʹΑΔఆՌ൜ࡑࢧԉʹ׆༻Ε Δɽ ͷΑͳഎܠͷԼɼΕ·Ͱଟͷา༰ख๏ఏ ҊΕΓɼΕΒେϞσϧϕʔεͱΞϐΞ ϥϯεϕʔεͷ 2 ख๏ʹΔͱͰΔɽ Ϟσϧϕʔεͷख๏ͰɼೖըʹϞσϧΛΊΔ 1 ⓒ 2013 Information Processing Society of Japan Vol.2013-CVIM-187 No.10 2013/5/30
Transcript of 7 s 2æ ݯ
IPSJ SIG Technical Report
1,a) 1,b) 2,c) 1,d)
Gait motion
descriptors
1.
12
1
Osaka University2
Drexel Universitya) [email protected]) [email protected]) [email protected]) [email protected]
[1]
DNA
ATM
[2]
2
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IPSJ SIG Technical Report
Urtasun
[3]
Spencer [4]
Han Bhanu[5]
Gait energy image (GEI)
Makihara [6]
(Frequency domain feature, FDF)
Bashir [8] 1
Gait entropy image
(GEnI) Lam [9]
Gait flow image (GFI)
Bashir [10] GEnI
GEI Masked GEI based on GEnI (MGEI)
Wang [11]
4 1 Chrono-gait
image (CGI)
Kobayashi Otsu[12]
251
(Cubic higher-order local auto-corelation,
CHLAC) Bashir [13]
Gait motion descriptor
(GMD)
Iwama
[14]
4,000
(GEI FDF GEnI GFI
MGEI CGI)
(1)
(2)
(3)
(a) GEI (b) FDF (c) GENI
(d) MGEI (e) GFI (f) CGI
(g) CHLAC
(h) GMD
1
2
2.
2.1
[7]
1
[6]
GEI
GEI
[14]
1(a) GEI
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FDF
1
FDF GEI
0 GEI
GEI 1(b)
FDF
GEnI GEI
GEnI
GEI
0
GEI
GEI
1(c)
GEnI
MGEI GEI GEnI
GEnI
GEI
MGEI
1(d) GEI GEnI
GFI
GFI
1(e) GFI
CGI 4 1
CGI
1(f) CGI
2.2
t
t+ n
CHLAC CHLAC
CHLAC
1(g) CHLAC
2.3
GMD
GFI
GMD
GMD
1(h)
GMD
GMD
3.
3.1
P G D
xPi ∈ R
D(i = 1, . . . , NP ) xGj ∈ R
D(j = 1, . . . , NG)
NP NG
P G (
) P i
xPi G j xG
j Mi,j
Mi,j =∥∥xP
i − xGj
∥∥ (1)
M Mi,j
M = mini,j
Mi,j (2)
3.2
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(a) PCA (b) LDA
2 GEI
(Linear
discriminant analysis, LDA)
(Principal component analysis, PCA)
(LDA)
2(a) GEI PCA 2
SW SB
SW =
Nclass∑
i=1
Nifeatures∑
j=1
(xi,j −mi)(xi,j −mi)T (3)
SB =
Nclass∑
i=1
N ifeatures(mi −m)(mi −m)T (4)
SWx = λSBx, x �= 0 (5)
Nclass N ifeatures i
xi,j i j
mi i m
2(b) GEI LDA 2
PCA ( 2(a)) LDA
( 2(b))
1 OU-ISIR Gait Database, The Treadmill Dataset
Dataset A 9 14 20
Dataset B 32 21 47
3 ( ) ( )
4.
4.1
The OU-ISIR Gait Database, The Treadmill
Dataset [15] Dataset A B Dataset
A 2km/h 10km/h 1km/h
34 Dataset B
32 68
1
3
DatasetA
2
Dataset B
1
31
4.2
1 1 (Vefirication) 1
N (Identification)
1 1
(False
rejection rate, FRR) (False acceptance
rate, FAR)
(Receiver
Operating Characteristics, ROC)
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(a) ROC (
)
(b) CMC (
)
(c) ROC ( ) (d) CMC ( )
(e) (f) 1
4
1 1
(Equal error rate, EER) 1 1
1 N N
N
(Cumulative matching characteristics, CMC)
CMC 3
90% 3
90%
1
4.3
4(a), (b)
ROC CMC 4(c), (d)
ROC CMC
4(e) (f)
1
GEI FDF
GEnI MGEI
( 4(a), (b) )
( 4(c),
(d)) GEI FDF GEnI MGEI
GEnI MGEI
GEI FDF
4(e), (f)
4.4
5 1 1 6 1 N
7(a) 7(b)
1
GFI GMD
GEI
FDF
5.
GEI
GMD
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(a) 2 km/h vs. 4 km/h (b) 2 km/h vs. 6 km/h
(c) 2 km/h vs. 8 km/h (d) 2 km/h vs. 10 km/h
(e) 4 km/h vs. 6 km/h (f) 4 km/h vs. 8 km/h
(g) 4 km/h vs. 10 km/h (h) 6 km/h vs. 8 km/h
(i) 6 km/h vs. 10 km/h (j) 8 km/h vs. 10 km/h
5 ROC
8(a) 8(d)
(a) 2 km/h vs. 4 km/h (b) 2 km/h vs. 6 km/h
(c) 2 km/h vs. 8 km/h (d) 2 km/h vs. 10 km/h
(e) 4 km/h vs. 6 km/h (f) 4 km/h vs. 8 km/h
(g) 4 km/h vs. 10 km/h (h) 6 km/h vs. 8 km/h
(i) 6 km/h vs. 10 km/h (j) 8 km/h vs. 10 km/h
6 CMC
GEI , GMD
8(b) 8(c) 8(e) 8(f)
8(g) 8(h)
GEI GMD 2
GMD
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(a) (b) 1
7 1
(a) (b) (c)
(d) (e) (f)
(g) (h)
8 : :
:
GEI GMD (g) (h)
GEI GFI
GMD GEI
9(a) 9(d)
(a) (b) (c)
(d) (e) (f)
(g) (h)
9 : :
:
GEI GMD (g) (h)
GEI GFI
GEI
GMD
9(b) 9(c) 9(e) 9(f)
9(g) 9(h)
GMD 2
GEI GMD
GEI GMD
GEI GMD
6.
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The OU-ISIR Gait Database, The Treadmill
Dataset
1 1 1 N
GEnI MGEI GMD
(1)
CHLAC GMD (2)
( HumanID Gait Database
[16] ) (3)
JSPS (S)21220003
[1] Uidai. http://uidai.gov.in/.
[2] How biometrics could change security. http:
//news.bbc.co.uk/2/hi/programmes/click_
online/7702065.stm, Oct. 2008.
[3] R. Urtasun and P. Fua. “3D Tracking for Gait Char-acterization and Recognition”. In Automatic Face andGesture Recognition, 2004. Proceedings. Sixth IEEE In-ternational Conference on, Vol. 1, pp. 17–22, May. 2004.
[4] S Nicholas and J Carter. “Towards pose invariant gaitreconstruction”. Proc. of the IEEE International Con-ference on Image Processing 2005, Vol. 3, pp. 261–264,September. 2005.
[5] J Han and B Bhanu. “Individual recognition using gaitenergy image”. IEEE Trans. on Pattern Analysis andMachine Intelligence, pp. 316–322, February 2006.
[6] Y Makihara, R Sagawa, Y Mukaigawa, T Echigo, andY Yagi. “Gait recognition using a view transformationmodel in the frequency domain”. Proc. of the 9th Eu-ropean Conf. on Computer Vision, pp. 151–163, May2006.
[7] Y Makihara and Y Yagi. “Silhouette Extraction basedon Iterative Spatio-temporal Local Color Transformationand Graph-Cut Segmentation”. Proc. of the 19th Int.Conf. on Pattern Recognition, pp. 1-4, Dec 2008.
[8] K Bashir, T Xiang, and S Gong. “Gait recognition usinggait entropy image”. in Proc. 3rd Int. Conf. Imagingfor Crime Detection and Prevention,, pp. 1–6, Decem-ber 2009.
[9] Toby HW Lam, K. H. Cheung, and James NK Liu.“Gait flow image: A silhouette-based gait representationfor human identification”. Pattern recognition, Vol. 44,No. 4, pp. 973–987, April 2011.
[10] K Bashir, T Xiang, and S Gong. “Gait recognition with-out subject cooperation”. Pattern Recognit. Letters,Vol. 31, No. 13, pp. 2052–2060, October 2010.
[11] C Wang, J Zhang, J Pu, X Yuan, and L Wang. “Chrono-gait image: a novel temporal template for gait recogni-tion”. Proc. of the 11th European Conf. on Computer
Vision, pp. 257–270, October 2010.
[12] T Kobayashi and N Otsu. “Action and simultaneousmultiple-person identification using cubic higher-orderlocal auto-correlation”. Proc of the 17th InternationalConference on Pattern Recognition, Vol. 4, pp. 741–744,August 2004.
[13] K Bashir, T Xiang, S Gong, and Q Mary. “Gait represen-tation using flow fields”. Proc. of the British MachineVision Conference 2009, September 2009.
[14] H Iwama, M Okumura, Y Makihara, and Y Yagi. “TheOU-ISIR Gait Database Comprising the Large Popula-tion Dataset and Performance Evaluation of Gait Recog-nition”. IEEE Trans. on Information Forensics and Se-curity, Vol. 7, pp. 1511–1521, October 2012.
[15] Y. Makihara, H. Mannami, A. Tsuji, M.A. Hossain,K. Sugiura, A. Mori, and Y. Yagi. The ou-isir gaitdatabase comprising the treadmill dataset. IPSJ Trans.on Computer Vision and Applications, Vol. 4, pp. 53–62, Apr. 2012.
[16] S Sarkar, P J Philips, Z Liu, I Robledo, P Grother, andK Bowyer. IEEE Transactions on Pattern Analysis andMachine Intelligence.
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