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The periocular biometric has been shown to be a biometric that is rich in texture and detail. These traits are important for biometric recognition, specifically from the feature extraction module. Traditional texture based feature extraction techniques extract features from an entire biometric image. However, a genetic and evolutionary extraction technique has been created that optimizes the location...
Feature extraction of multispectral iris images relies heavily on location and dimensions to effectively extract the most relevant features. For recognition, iris images are normally captured in the 700nm to 900nm range. This is due to the fact that ranges in the normal 350–700nm range cannot so easily penetrate darker colored irises to get texture information, whereas larger ranges can. There have...
Genetic and Evolutionary Feature Extraction (GEFE) is a feature extraction technique that can be used to optimize feature extractors for any uniquely textured image. This technique has been extensively used with the Local Binary Pattern feature extraction algorithm for biometric recognition. This work proposes the combination of GEFE and the Local Ternary Pattern feature extraction technique. GEFE+LTP...
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