14) Ogihara ATM 15) ATM 10 16) 17),18) 1 4) 1 8),9) 10) 12) realadaboost 13) % 12) 2. 3 Gluhchev 19) 1 19) 2 10) 12) 3. 2 ID 1 8) 9),20) 2 2

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1 1, realadaboost % Biometric Person Authentication Method Using Pen Holding Feature Daigo Muramats, 1, 1 Yuki Hashimoto 1 and Hiroyuki Ogata 1 We focus on a biometric person authentication method using features of pen holding style. The manner of holding pen can be distinctive among persons and be useful modality for person authentication, because the manner is affected by both the physical features and habitual behavior. In order to evaluate the efficiency, we extract several features from the pen-holding image, and fuse them for verification. In this paper, realadaboost algorithm is used for the fusion, and user-dependent threshold is applied for a decision making. The developed algorithm is evaluated using the database collected dorm 30 persons. The algorithm achieved an EER of 4.0% against the impersonation attacks. 1. 1) ATM 2) 1) 3) 4) 5) 6) 7) 1 1 Department of Electrical and Mechanical Engineering, Seikei University 1 Presently with The Institute of Science and Industrial Research, Osaka University 2 Graduate School of Science and Technology, Seikei University 1 1

2 14) Ogihara ATM 15) ATM 10 16) 17),18) 1 4) 1 8),9) 10) 12) realadaboost 13) % 12) 2. 3 Gluhchev 19) 1 19) 2 10) 12) 3. 2 ID 1 8) 9),20) 2 2

3 情報処理学会研究報告䊕䊮㗔 Ꮐ 㗔 ฝ 㗔 ਅ 䊕䊮㗔䊕䊮 ᜰ㗔図 4 取得画像図 3 撮影設定図 2 ペン持ち方認証アルゴリズム徴を抽出し参照データとして登録するまた本研究では登録された本人データと他人データとのスコアを計算しこれらのスコアを利用して認証時に必要なパラメータの学習 (a) (b) (c) (d) (e) (f) (h) (i) 設定を行っている認証フェーズではデータ取得によって得られた画像から登録フェーズ同様特徴を抽出し抽出した特徴を用いて非類似度を計算するその後計算された非類似度を統合して認証スコアを計算ししきい値と比較することで認証を行う本章の残りの部分ではこれらのフェーズを構成する個々の処理について説明を行う 3.1 データ取得カメラを用いてペンを持った手を撮影する撮影方向はいくつか考えられるがペン持ち方の違いがわかりやすいよう本研究では図 3 に示すようにペンを持つ手を親指側から撮影するこれにより図 5 のような画像が取得される親指側から撮影することにより親指の位置や人差指の曲げ方の違いに関係する特徴を画像から取得できる 3.2 特徴抽出 (g) 取得された画像データから個人性が現れると思われる特徴を抽出する図 5 は異なる 9 図5 様々なペン持ち方人から取得したペン持ち方の画像であるこれらの図よりペン持ち方にはかなり個人差があ 3 c 2011 Information Processing Society of Japan

4 1 2 3 4 5 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) x y ( 9 ) 2 ( 10 ) - ( 11 ) x y ( 12 ) x y ( 13 ) x y ( 14 ) x y ( 15 ) x y ( 16 ) x

4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) x y ( 9 ) 2 ( 10 ) - ( 11 ) x y ( 12 ) x y ( 13 ) x y ( 14 ) x y ( 15 ) x y ( 16 ) x y ( 17 ) ( 18 ) ( 19 ) ( 20 ) x y ( 1 ) ( 2 ) A B F a = (fa 1, fa 2,...fa Nv ), F b = (fb 1, fb 2,..., fb Nv ) n dis n(a, B) = fa n fb n, n = 1, 2,..., N v (1) N v = 31 I r (i, j), 1 i W,1 j H I v(i, j), 1 i WF,1 j H F (H < H F, W < W F ) dis thumb = 1 W H min (x,y) Region W i=1 j=1 H I r (i, j) I v (x + i, y + j) (2) Resion (x g, y g ) (x g W/2 10, y g H/2 10) (x g + W/2 + 10, y g + H/2 + 10) N dim = 33 4

5 id {1, 2,..., N ID } R (id) Q Dis(R (id) Step 1, Q) = {disn(r(id), Q)}N n=1 Dis(R (id), Q) Dis(R (id), Q ) = (dis 1 (R (id), Q ),..., dis Ndim (R (id), Q )) Step 2 dis n (R (id), Q ) = disn(r(id), Q) norm (id) n, n = 1, 2,..., N dim (3) [ 1, 1] S S(R (id), Q ); a, b) = (s 1 (R (id), Q ; a, b),..., s Ndim (R (id), Q ; a, b) g(s; a, b) g(s; a, b) = 1 s n(r (id), Q; a, b) = g(dis(r(id), Q); a, b) (4) exp( a(s b)) a, b [ 1, 1] Step 3 Step2 S s n n s n Score Ndim Score(R (id), Q; Θ) = n=1 (5) α ns n(r (id), Q; a, b) (6) Θ = {α n} N dim n=1 α n n Θ realadaboost 13) ) ) T hreshod id (c) = T h id + c dev id (7) T h id dev id id Z-norm 3) 3.6 Q id X X = { Accepted Rejected if Score(R (id), Q ) > T hreshold id (c) otherwise [step 1] [step 2] 1 [step 3] [step 4] [step 5] [step 6] step (30 8) = (8) 5

1 No. T h id dev id EER [%] 1 12.0 2 10.7 3 8.7 4-11.4 5-12.0 6-12.0 7 6.0 8 4.1 9 4.1 10 11.3 4.

6 1 No. T h id dev id EER [%] id Gd (id) l, 1 id 30, 1 l 10 id Ad (id) k, 1 k K id, K id {40, 45} Gd (id) 1 R (id), 2 l 5 Gd (m) Gd (id) l l = Gd(id) 1 ; m id, 1 l 10 Q id Gd (id) j, 6 j 10 Ad (id) k, 1 k K i N G = 150 N I = False Reject Rate FRR False Accept Rage FAR Equal Error Rate EER F RR(T hreshold(c)) = 1 N G F AR(T hreshold(c)) = 1 N I id=1 l=6 K 10 i δ i=1 k=1 ( ) δ Score(Gd (id) 1, Gd (id) ; Θ) < T hreshold(c) ( Score(Gd (id) l ) 1, Ad (id) ; Θ) T hreshold(c) k δ(x) { 1 if x is true δ(x) =. (9) 0 otherwise EER : EER = F AR(T hreshold(c )) + F RR(T hreshold(c ))) 2 (10) where c = argmin F AR(T hreshold(c)) F RR(T hreshold(c)) (11) c % FAR 6

7 realadoboost 9 8 realadaboost 4.1% Znorm 1-3 Znorm ) 12) EER=5.6% 1 7 1) Jain, A. K., Flynn, P. and Ross, A. A.: Handbook of Biometrics, Springer Science+Business Media, LLC. (2008). 2) Vol.207, No.3, pp (2006). 3) Ross, A. A., Nandakumar, K. and Jain, A. K.: Handbook of Multibiometrics, Springer Science+Business Media, LLC. (2006). 4) Vol.21, No.1, pp (2010). 5) Brunelli, R. and Falavigna, D.: Person identification using multiple cues, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.17, No.10, pp (1995). 6) D Vol.J92-D, No.8, pp (2009). 7) Zhou, X., Bhanu, B. and Han, J.: Human Recognition at a Distance in Video by Integrating Face Profile and Gait (2005). 8) Munich, M.E. and Perona, P.: Visual Identification by Signature Tracking, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.25, No.2, pp (2003). 9) Yasuda, K., Muramatsu, D., Shirato, S. and Matsumoto, T.: Visual-based online signature verification using features extracted from video, Journal of Network and Computer Applications, Vol.33, pp (2010). 10) A Vol.J92-A, No.5, pp (2009). 11) Hashimoto, Y., Muramatsu, D. and Ogata, H.: Biometric person authentication method using features extracted from pen- holding style, Proc. SPIE, Vol ) Hashimoto, Y., Muramatsu, D. and Ogata, H.: 7

8 Vol.34, No.54, pp.1 4 (2010). 13) Schapire, R.E. and Singer, Y.: Improved Boosting Algorithms Using Confidencerated Predictions, Machine Learning, Vol.37, No.3, pp (1999). 14) Sidlauskas, D.P. and Tamer, S.: Hand Geometry Recognition, Handbook of Biometrics (Jain, A.K., Flynn, P. and Ross, A.A., eds.), Springer Science+Business Media, LLC., chapter5 (2008). 15) Ogihara, A., Matsumura, H. and Shiozaki, A.: Biometric verification using keystroke motion and key press timing for ATM user authentication, pp (2006). 16) : (2009) 17) Hangai, S. and Higuchi, T.: Writer Identification Using Finger-Bend in Writing Signature, Proc. BioAW2004, LNCS, Vol.3087, pp (2004). 18) Kamel, N., Sayeed, S. and Ellis, G.: Glove-Based Approach to Online Signature Verification, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.30, No.6, pp (2008). 19) Gluhchev, G., Savov, M., Boumbarov, O. and Vasileva, D.: A New Approach to Signature-Based Authentication, ICB, Lecture Notes in Computer Science, Vol.4642, Springer, pp (2007). 20) Shirato, S., Muramatsu, D. and Matsumoto, T.: Camera-based online signature verification system: effects of camera positions, World Automation Congress (WAC), 2010, pp.1 6 (2010). 21) Jain, A.K., Griess, F.D. and Connell, S.D.: On-line signature verification, Pattern Recognition, Vol.35, pp (2002). 8

Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U

Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U Computer Security Symposium 017 3-5 October 017 1,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) 1. 017 5 [1] 1 Meiji University Graduate School of Advanced Mathematical Science