103 122 103 88 7 * ** ** * (Feature Extraction System) (Fourier Transform) * **
104 [2-7] [4-7] [8-9] [7,10-11] [12-13] [14];[15] (Fourier Descriptors, FD) ;[16] ;[17] ;Wu[18] B (B-spline) 6 : (edge enhancement) (edge detection) [19] 2.1 (sliding) (stretch) ( ) / / ( ) [19-20] ( ) P P (mask) [19] ( )
105 P P ( ) 2.2 (Sobel) (Gx Gy) ( ) (thinning)[19] ( ) (pattern representation) 3.1 3.1 3.2(b) 3.2(c) 3.3 3.4
106
107 3.2 3.5 3.6 3.7 3.7(d) 3.7(e) 3.7(f)
108 3.8
109 3.3 3.9 0-255 3.10 3.11
110 3.4
111 3.12 ( ) 3.13 ( ) (pattern recognition) 4.1 (boundary representation) (region representation) (moment representation) (chain codes)b AR (autogressive model)
112 AR 4.2 (Nyquist sampling theorem) (band limited signal) (discrete Fourier transform) (fast Fourier transform) N (N=32) N (N/2)(N/2)-1 N-2 ( ) Qc P Qc P X 2 /N N α α f(t) Qc t0 F(u) 1( F(t) 1 t0 ) F(u) F(u) 2 F(u) = F(u) *e j(1 +2) F(u) F(u) u = 0 ~ (2 k 1) F(0) (normalized feature spectrum) 2 k = N 4.3 (statistical pattern recognition) (neural network pattern recognition)
113 (Euclidean distance) 4.4 ( ) ( ) 1 ( ) 1 ( 1 ) ( ) ( ) (spatial index) (spatial database) (Computer Aided Design) (Geographical Information System) N(N=4) (N-dimensional hyperplane ) (hyper-polygon region) (norm) 4096
114 5 5.1 i (level i0i3) (link) i+1 norm j ( j+1 1j4) 01 ( 0.125*K)(0.125*(K+1)) K+1 ( 18) K=0,1,2, 7 16 5.2 4 norm norm ( lower bound + upper bound) / 2 16
115 N1N2N3 L L N1 N1L N1R N1 N2 N2 (N2L,N2R) N1L N1R i 3 i i=1 234 M M M M M i 5 i i=1234 M M M 1 M
116
117
118 O, O Tb(O) O D D M ( ) 5 4 3 2 Ti = 7 i= 1 Wi / Di O Tb(i) Di=Dmax Dmax Tb(i) D Ti K( )
119 32 ( ) R-tree [1] Chellappa, R., Wilson, C.L., and Sirohey, S. A. (1995) Human and machine recognition of faces: a survey. Proceedings of the IEEE 83 (5), 705-740. [2] Sakai, T., Nagao, M., and Fujibayashi, S. (1969) Line extraction and pattern recognition in a photograph. Patt. Recog. 1, 233-248.
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121 [16] Harmon, L., and Hunt, W. (1997) Automatic recognition of human face profile. Computer Graphic and Image Process. 6, 135-156. [17] Harmon, L., Khan, M., Lasch, R., and Ramig, P. (1981) Machime identification of human faces. Patt. Recog. 13, 97-110. [18] Wu, C. and Huang, J. (1990) Human face profile recognition by computer. Patt. Recog. 23, pp. 255-259. [19] Gonzalez, R.C., and Woods, R.E. (1992) Digital image processing. Addison-Wesley. [20] Lindley, C.A. (1994) Practical image processing in C. Big Apple Tuttle-Mori.
122 An Application of Image Database on Facial Recognition The Database of Lawbreakers Fang-Yie Leu * Shau-Tang Yu ** Min-Shiung Shiu ** Chau-Li Lin * Abstract The goal of this project is to construct a human image database of lawbreakers. After the police get the human image drawn by the witness, they can use the human image database and retrieval system to find out the possible upsets. At first, we should construct the image data of lawbreakers, then scan the standard mug shot taken by the police and transfer it to the image file. After retrieving the features of face organs including eyes, eyebrows, lips, outline of face and profile, by using edge enhancement, Sober edge detection, Hough transform and thinning techniques, we can obtain the outlines of each organ. The outlines are then transform into Fourier descriptors with Fourier Transform. After that a normalized feature spectrum is generated. The second to fifth spectrums are extracted to construct organ relations. We also construct the spatial index for every relation. The human image drawn by the witness is also proceeded by the same techniques described above. We use the image to query the spatial index to downsize the searching space. The importance of weight of each organ is also discussed in this article. Keyword: Image Database, Image Database of Lawbreakers, Face Reconization * Institute of Computer and Information Sciences, Tunghai University, Taichung 407, TAIWAN. ** Department of Computer and Information Sciences, Tunghai University, Taichung 407, TAIWAN