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| Li Ma, Tieniu Tan, Yunhong Wang, Dexin Zhang, "Personal Identification Based on Iris Texture Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519-1533, December, 2003. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.2003.1251145, author = {Li Ma and Tieniu Tan and Yunhong Wang and Dexin Zhang}, title = {Personal Identification Based on Iris Texture Analysis}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {25}, number = {12}, issn = {0162-8828}, year = {2003}, pages = {1519-1533}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2003.1251145}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Personal Identification Based on Iris Texture Analysis IS - 12 SN - 0162-8828 SP1519 EP1533 EPD - 1519-1533 A1 - Li Ma, A1 - Tieniu Tan, A1 - Yunhong Wang, A1 - Dexin Zhang, PY - 2003 KW - Iris recognition KW - image quality assessment KW - multichannel spatial filters KW - texture analysis KW - biometrics. VL - 25 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.
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