生物特徵辨識技術主要利用生物個體、如人類，其特徵的差異性，來達到區分與辨識生物個體之目的。近年來是個應用相當廣泛，且十分熱門的技術。生物特徵主要包括指靜脈、指紋、虹膜、視網膜、手掌紋、語音、臉、體形等。而其中又以指紋辨識發展最為長久與全面，主要原因為人類手指特徵擷取方便，手指個數也最多，因此更能增加辨識技術之安全性。然而，指紋暴露在外容易受到破壞，且十分容易遭到仿冒。所以，手指靜脈遂成為生物特徵辨識技術的新寵兒。除了保留利用手指便於擷取特徵的優點，由於靜脈位於手指指內，不容易被外在因素干擾。另一方面，若靜脈內血液停止流動，將因無紅外線反射而偵測不到靜脈之結構與形狀，進而達到生物活體防偽的功能。基於以上原因本篇論文提出指靜脈辨識系統，結合指靜脈形成的拓普結構與影像品質量尺測量指靜脈影像的相似度，利用其互補的特性達到良好的辨識效果。基於公開的與自製的指靜脈資料庫，實驗結果顯示本篇論文所提出的指靜脈辨識系統的辨識率優於其它已發表之指靜脈辨識系統。因此，本系統有極大的潛力與競爭力能應用於生活的各個層面，以造福人群。本論中使用之指靜脈紅外線擷取設備有著極低成本的優點，但也因此造成了所拍攝的指靜脈影像模糊與低對比，所以在辨識個體的指靜脈之前，影像首先需要使用對比增強法處理。因此，本論文提出一種區域式對比增強的方法以改善影像過度與不足的對比增強。此方法首先保留區域式對比增強的效果，但利用非重疊式的影像分割法，加速傳統區域式對比增強的執行速度；接著，利用雙向貝齊爾曲線將子影像的連續累積分佈進行平滑化，以改善區域式的對比增強中，所產生的過度增強不良效果。最後，利用權重關係結合各子影像的轉換函數，利用此轉換函數增強影像對比。基於不同特性的測試影像與各種主客觀評估，此方法能大幅改善模糊影像的對比，提供良好的辨識效率，以利於實際應用在辨識系統之上。我們利用所提出的對比增強方法增進擷取到的指靜脈可辨識性與辨識系統之效能。

Various personal authentication systems have been extensively used in numerous civilian
applications because of the continual growth in the demands on security systems in
recent years. Biometrics has received considerable attention and has been extensively
used for identifying individuals in personal authentication systems. This thesis presents
a novel finger-vein recognition system based on the enhanced finger-vein images. To
achieve this, the system first identifies regions-of-interest from the captured images, and
then determines their skeleton topologies, which are used to analyze the similarities and
differences between finger-vein patterns. The system exhibited encouraging experimental
results in differentiating individuals, but failed in classifying some extreme cases of ambiguous
features. Consequently, an additional image quality assessment stage is borrowed
to enhance the recognition accuracy. As demonstrated in the experimental results, the
proposed extended strategy substantially improves upon the skeleton topology matchingonly
approach. The performance of the proposed method outperforms the existing systems
with the published databases and our own databases. The twofold examined finger-vein
recognition system exhibits great potential as a competitive biometric, and thus the practical
applications of which are vast. However, the captured finger-vein images are blurred
and low-contrast by the implemented low-cost near-infrared imaging device. Therefore,
the captured finger-vein image needs to be enhanced by the proposed contrast enhancement
method. Consequently, this thesis presents a novel local histogram equalization by
combining the transformation functions of the non-overlapped sub-images based on the
gradient information for edge preservation and better visualization. To ameliorate the
problems of the over- and under-enhancement produced by conventional local histogram
equalization, the bilateral Bezier curve-based histogram modification strategy is first employed
to modify the significant and insufficient changes of each cumulative distribution
in each sub-image. Yet, the gradient information has not been considered, and the cumulative
distribution of some enhanced sub-images are still significant or insufficient because
of the over- and under-enhancement, respectively. Therefore, the key insight of the proposed
method is that the transformation functions of the partitioned sub-images will be
weighed and combined based on the proportion of gradients to preserve the image texture.
In addition, the input image is separated into the non-overlapped sub-images for reducing
the time complexity. Based on the eight representative test images and mean opinion
score, the experimental results demonstrate that the proposed method is quite competitive
with four state-of-the-art histogram equalization methods in the literature. Furthermore,
according to the subjective evaluation, it is observed that the proposed method can also
apply to the practical applications and achieve good visual quality.

[1] A. Kumar and K. Prathyusha, “Personal authentication using hand vein triangulation
and knuckle shape,” Image Processing, IEEE Transactions on, vol. 18, no. 9,
pp. 2127–2136, 2009.
[2] H. Lee, S. Lee, T. Kim, and H. Bahn, “Secure user identification for consumer
electronics devices,” Consumer Electronics, IEEE Transactions on, vol. 54, no. 4,
pp. 1798–1802, 2008.
[3] K. Lee and H. Byun, “A new face authentication system for memory-constrained
devices,” Consumer Electronics, IEEE Transactions on, vol. 49, no. 4, pp. 1214–
1222, 2003.
[4] W. Li, J. You, and D. Zhang, “Texture-based palmprint retrieval using a layered
search scheme for personal identification,” Multimedia, IEEE Transactions on,
vol. 7, no. 5, pp. 891–898, 2005.
[5] D. Zhang, W. Kong, J. You, and M. Wong, “Online palmprint identification,” Pattern
Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, no. 9, pp. 1041–
1050, 2003.
[6] H. Proenca, “Iris recognition: On the segmentation of degraded images acquired in
the visible wavelength,” Pattern Analysis and Machine Intelligence, IEEE Transactions
on, vol. 32, no. 8, pp. 1502–1516, 2010.
[7] R. Cappelli, D. Maio, A. Lumini, and D. Maltoni, “Fingerprint image reconstruction
from standard templates,” Pattern Analysis and Machine Intelligence, IEEE Transactions
on, vol. 29, no. 9, pp. 1489–1503, 2007.
[8] T. Jea and V. Govindaraju, “A minutia-based partial fingerprint recognition system,”
Pattern Recognition, vol. 38, no. 10, pp. 1672 – 1684, 2005.
[9] A. Kumar and Y. Zhou, “Human identification using finger images,” Image Processing,
IEEE Transactions on, vol. 21, no. 4, pp. 2228–2244, 2012.
[10] F. Liu, G. Yang, Y. Yin, and X. Xi, “Finger-vein recognition based on fusion of
pixel level feature and super-pixel level feature,” in Biometric Recognition (Z. Sun,
S. Shan, G. Yang, J. Zhou, Y. Wang, and Y. Yin, eds.), vol. 8232 of Lecture Notes in
Computer Science, pp. 274–281, Springer International Publishing, 2013.
[11] Z. Liu and S. Song, “An embedded real-time finger-vein recognition system for mobile
devices,” Consumer Electronics, IEEE Transactions on, vol. 58, no. 2, pp. 522–
527, 2012.
[12] N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns
based on repeated line tracking and its application to personal identification,” Mach.
Vision Appl., vol. 15, pp. 194–203, Oct. 2004.
[13] K. R. Park, “Finger vein recognition by combining global and local features based
on svm,” Computing and Informatics, pp. 295–309, 2011.
[14] H. Qin, C. Yu, and L. Qin, “Region growth–based feature extraction method for
finger-vein recognition,” Optical Engineering, vol. 50, no. 5, pp. 057208–057208–
8, 2011.
[15] W. Song, T. Kim, H. Kim, J. Choi, H. Kong, and S. Lee, “A finger-vein verification
system using mean curvature,” Pattern Recognition Letters, vol. 32, no. 11, pp. 1541
– 1547, 2011.
[16] D. Wang, J. Li, and G. Memik, “User identification based on finger-vein patterns
for consumer electronics devices,” Consumer Electronics, IEEE Transactions on,
vol. 56, no. 2, pp. 799–804, 2010.
[17] J. Wu and C. Liu, “Finger-vein pattern identification using {SVM} and neural network
technique,” Expert Systems with Applications, vol. 38, no. 11, pp. 14284 –
14289, 2011.
[18] J. Wu and S. Ye, “Driver identification using finger-vein patterns with radon transform
and neural network,” Expert Systems with Applications, vol. 36, no. 3, Part 2,
pp. 5793 – 5799, 2009.
[19] Y. Xin, Z. Liu, H. Zhang, and H. Zhang, “Finger vein verification system based on
sparse representation.,” Appl Opt, vol. 51, no. 25, pp. 6252–8, 2012.
[20] W. Yang, G. Ma, W. Li, and Q. Liao, “Finger vein verification based on neighbor
pattern coding.,” IEICE Transactions on Information and Systems, vol. 96-D, no. 5,
pp. 1227–1229, 2013.
[21] W. Yang, Q. Rao, and Q. Liao, “Personal identification for single sample using finger
vein location and direction coding,” in Hand-Based Biometrics (ICHB), 2011
International Conference on, pp. 1–6, Nov 2011.
[22] P. Campbell and S. Abhyankar, “Fractals, form, chance and dimension,” The Mathematical
Intelligencer, vol. 1, no. 1, pp. 35–37, 1978.
[23] Y. Yin, L. Liu, and X. Sun, “Sdumla-hmt: A multimodal biometric database.,” in
CCBR (Z. Sun, J.-H. Lai, X. Chen, and T. Tan, eds.), vol. 7098 of Lecture Notes in
Computer Science, pp. 260–268, Springer, 2011.
[24] D. Hartung, A. Pflug, and B. C., “Vein pattern recognition using chain codes spatial
information and skeleton fusing,” in Sicherheit’12, pp. 245–256, 2012.
[25] J. Yang, Y. Shi, J. Yang, and L. Jiang, “A novel finger-vein recognition method with
feature combination,” in Image Processing (ICIP), 2009 16th IEEE International
Conference on, pp. 2709–2712, Nov 2009.
[26] D. Zhang, Z. Guo, G. Lu, L. Zhang, Y. Liu, and W. Zuo, “Online joint palmprint and
palmvein verification,” Expert Systems with Applications, vol. 38, no. 3, pp. 2621 –
2631, 2011.
[27] Z. Wang, E. Simoncelli, and A. Bovik, “Multi-scale structural similarity for image
quality assessment,” in in Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers,
(Asilomar, pp. 1398–1402, 2003.
[28] R. Gonzalez and R. Woods, Digital Image Processing. Boston, MA, USA: Addison-
Wesley Longman Publishing Co., Inc., 2nd ed., 2001.
[29] J. Stark, “Adaptive image contrast enhancement using generalizations of histogram
equalization,” Image Processing, IEEE Transactions on, vol. 9, no. 5, pp. 889–896,
2000.
[30] M. Abramowitz and I. Stegun, Handbook of mathematical functions: with formulas,
graphs, and mathematical tables. Courier Dover Publications, 2012.
[31] J. Astola, P. Haavisto, and Y. Neuvo, “Vector median filters,” Proceedings of the
IEEE, vol. 78, no. 4, pp. 678–689, 1990.
[32] L. Lam, S. Lee, and C. Suen, “Thinning methodologies-a comprehensive survey,”
Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 14, no. 9,
pp. 869–885, 1992.
[33] N. Miura, A. Nagasaka, and T. Miyatake, “Extraction of finger-vein patterns using
maximum curvature points in image profiles,” IEICE - Trans. Inf. Syst., vol. E90-D,
pp. 1185–1194, Aug. 2007.
[34] L. Zhang, R. Zhang, and Y. C.B., “Study on the identity authentication system on
finger vein,” in Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008.
The 2nd International Conference on, pp. 1905–1907, May 2008.
[35] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from
error visibility to structural similarity,” Image Processing, IEEE Transactions on,
vol. 13, no. 4, pp. 600–612, 2004.
[36] A. Celebi, R. Duvar, and O. Urhan, “Fuzzy fusion based high dynamic range imaging
using adaptive histogram separation,” IEEE Transactions on Consumer Electronics,
vol. 61, no. 1, pp. 119–127, 2015.
[37] H. Choi and P. Rhee, “Head gesture recognition using hmms,” Expert Systems with
Applications, vol. 17, no. 3, pp. 213–221, 1999.
[38] A. De la Torre, A. Peinado, J. Segura, J. Perez-Cordoba, M. Benitez, and A. Rubio,
“Histogram equalization of speech representation for robust speech recognition,”
IEEE Transactions on Speech and Audio Processing, vol. 13, no. 3, pp. 355–366,
2005.
[39] A. Franco and L. Nanni, “Fusion of classifiers for illumination robust face recognition,”
Expert Systems with Applications, vol. 36, no. 5, pp. 8946–8954, 2009.
[40] U. Ghanekar, A. Singh, and R. Pandey, “A contrast enhancement-based filter for
removal of random valued impulse noise,” IEEE Signal Processing Letters, vol. 17,
no. 1, pp. 47–50, 2010.
[41] M. Kang, B. Kim, and K. Sohn, “Ciecam02-based tone mapping technique for color
image contrast enhancement,” Optical Engineering, vol. 48, no. 8, p. 087001, 2009.
[42] W. Kao, J. Ye, M. Chu, and C. Su, “Image quality improvement for electrophoretic
displays by combining contrast enhancement and halftoning techniques,” IEEE
Transactions on Consumer Electronics, vol. 55, no. 1, pp. 15–19, 2009.
[43] S. Pei, Y. Zeng, and C. Chang, “Virtual restoration of ancient chinese paintings using
color contrast enhancement and lacuna texture synthesis,” IEEE Transactions on
Image Processing, vol. 13, no. 3, pp. 416–429, 2004.
[44] A. Wahab, S. Chin, and E. Tan, “Novel approach to automated fingerprint recognition,”
IEE Proceedings on Vision, Image and Signal Processing, vol. 145, no. 3,
pp. 160–166, 1998.
[45] C. Lee, C. Lee, and C. Kim, “Contrast enhancement based on layered difference
representation of 2d histograms,” IEEE Transactions on Image Processing, vol. 22,
no. 12, pp. 5372–5384, 2013.
[46] M. Nikolova and G. Steidl, “Fast hue and range preserving histogram specification:
Theory and new algorithms for color image enhancement,” IEEE Transactions on
Image Processing, vol. 23, no. 9, p. 4087–4100, 2014.
[47] J. Shin and R. Park, “Histogram-based locality-preserving contrast enhancement,”
IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1293–1296, 2015.
[48] S. Wang, J. Zheng, H. Hu, and B. Li, “Naturalness preserved enhancement algorithm
for non-uniform illumination images,” IEEE Transactions on Image Processing,
vol. 22, no. 9, pp. 3538–3548, 2013.
[49] H. Xu, S. Jiao, G. Zhai, X. Wu, and X. Yang, “Generalized equalization model for
image enhancement,” IEEE Transactions on Multimedia, vol. 16, no. 1, pp. 68–82,
2013.
[50] S. Huang, F. Cheng, and Y. Chiu, “Efficient contrast enhancement using adaptive
gamma correction with weighting distribution,” IEEE Transactions on Image Processing,
vol. 22, no. 3, pp. 1032–1041, 2013.
[51] R. Gonzalez and R. Woods, Digital Image Processing (2nd). Addison-Wesley Longman
Publishing Co., Inc., Boston, MA, USA, 2001.
[52] S. Chen and A. Ramli, “Contrast enhancement using recursive mean-separate histogram
equalization for scalable brightness preservation,” IEEE Transactions on
Consumer Electronics, vol. 49, no. 4, pp. 1301–1309, 2003.
[53] F. Cheng and S. Huang, “Efficient histogram modification using bilateral bezier
curve for the contrast enhancement,” Journal of Display Technology, vol. 9, no. 1,
pp. 44–50, 2013.
[54] M. Kim and M. Chung, “Recursively separated and weighted histogram equalization
for brightness preservation and contrast enhancement,” IEEE Transactions on
Consumer Electronics, vol. 54, no. 3, pp. 1389–1397, 2008.
[55] J. Kim, L. Kim, and S. Hwang, “An advanced contrast enhancement using partially
overlapped sub-block histogram equalization,” IEEE Transactions on Circuits and
Systems for Video Technology, vol. 11, no. 4, pp. 475–484, 2001.
[56] Y. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,”
IEEE Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1–8, 1997.
[57] F. Lamberti, B. Montrucchio, and A. Sanna, “Cmbfhe: a novel contrast enhancement
technique based on cascaded multistep binomial filtering histogram equalization,”
IEEE Transactions on Consumer Electronics, vol. 52, no. 3, pp. 966–974, 2006.
[58] P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially
adaptive method for mammographic contrast enhancement,” Physics in Medicine
and Biology, vol. 48, no. 6, p. 787, 2003.
[59] K. Sim, C. Tso, and Y. Tan, “Recursive sub-image histogram equalization applied
to gray scale images,” Pattern Recognition Letters, vol. 28, no. 10, pp. 1209–1221,
2007.
[60] Y. Wang, Q. Chen, and B. Zhang, “Image enhancement based on equal area dualistic
sub-image histogram equalization method,” IEEE Transactions on Consumer
Electronics, vol. 45, no. 1, pp. 68–75, 1999.
[61] T. Celik, “Spatial entropy-based global and local image contrast enhancement,”
IEEE Transactions on Image Processing, vol. 23, no. 12, pp. 5298–5308, 2014.
[62] T. Celik and T. Tjahjadi, “Contextual and variational contrast enhancement,” IEEE
Transactions on Image Processing, vol. 20, no. 12, pp. 3431–3441, 2011.
[63] A. Masmoudi, R. Trabelsi, M. Krid, and D. Masmoudi, “Implementation of a fingervein
recognition system based on improved gaussian matched filter,” MAGNT
Research Report, vol. 2, no. 4, pp. 251–260, 2014.
[64] W. Yang, X. Yu, and Q. Liao, “Personal authentication using finger vein pattern and
finger-dorsa texture fusion,” in Proceedings of the 17th ACM International Conference
on Multimedia, MM ’09, (New York, NY, USA), pp. 905–908, ACM, 2009.
[65] T. Ong, J. Teng, K. Muthu, and A. Teoh, “Multi-instance finger vein recognition
using minutiae matching,” in Image and Signal Processing (CISP), 2013 6th International
Congress on, vol. 03, pp. 1730–1735, Dec 2013.
[66] P. Tsai, C. Liang, T. Huang, and H.-H. Chen, “Image enhancement for backlightscaled
tft-lcd displays,” IEEE Transactions on Circuits and Systems for Video Technology,
vol. 19, no. 4, pp. 574–583, 2009.
[67] S. Chen and A. Ramli, “Minimum mean brightness error bi-histogram equalization in
contrast enhancement,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4,
pp. 1310–1319, 2003.
[68] M. Eramian and D. Mould, “Histogram equalization using neighborhood metrics,”
in Proceedings of The 2nd Canadian Conference on Computer and Robot Vision,
pp. 397–404, 2005.
[69] K. Panetta, E. Wharton, and S. Agaian, “Human visual system-based image enhancement
and logarithmic contrast measure,” IEEE Transactions on Systems, Man, and
Cybernetics, Part B: Cybernetics, vol. 38, no. 1, pp. 174–188, 2008.
[70] KODAK, “Kodak test images database,” (Online) http://r0k.us/graphics/kodak/,
1999.