I'm so addicted to bags! I got a bunch of collection of celine outlet bags! Some of my friend call me that im a hoarder of bags! Bags is my happiness! Sometimes i used to brought 10 different purses in just a month! And when i saw the celine bag on internet and its always using by kyllie jenner im falling in love with these bag! And everytime that i had a new bag i felt like im not a 100% satisfied! This celine inspired bag has a different eagerness on me but i dont want to spend a thousand dollar just for a purse and when i saw this im so happy and excited! But i got hesitation on the size, but when i read the review, lots of people had a compliment on this bag thats why i choose this rather than to have a regrets on the end, when i received this its soooo cute! Looked like a sling bag nano type of celine bag yeah its not really big like what i want but still its ok! The quality! Its really impressive! Looked like real leather the stitches! Perfect! And its sooooo classy! Looking forward to have the tote style.
In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation in detail. Secondly, three strategies are described, which are orientation probability distribution function based strategy to delete some redundant feature keypoints, magnitude probability distribution function based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature.
Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases CASIA-V3 Interval, Lamp, and, demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.