Rotation Invariant Texture Features and Their Use in Automatic Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gender and Ethnic Classification of Face Images
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
Fuzzy 3D Face Ethnicity Categorization
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
A study on gait-based gender classification
IEEE Transactions on Image Processing
Ethnic classification based on iris images
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Local feature based retrieval approach for iris biometrics
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Iris pattern is commonly regarded as a kind of phenotypic feature without relation to the genes. In this paper, we propose a novel ethnic classification method based on the global texture information of iris images. So we would argue that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In our scheme, a bank of multichannel 2D Gabor filters is used to capture the global texture information and AdaBoost is used to learn a discriminant classification principle from the pool of the candidate feature set. Finally iris images are grouped into two race categories, Asian and non-Asian. Based on the proposed method, we get an encouraging correct classification rate (CCR) of 85.95% on a mixed database containing 3982 iris samples in our experiments.