Identification of woven fabric motifs using the K-fold cross validation test with two stages, namely the training and testing stages. The image data of the woven fabric used is the image of woven fabric from 3 tribes of TTS district, namely the Amanatun, Amanuban, and Mollo tribes. In this study, digital image processing is used to identify the type of woven fabric in the TTS district using the HSV color feature extraction method, and the GLCM texture feature, and to measure the similarity of woven fabric using the Euclidean distance method. The many types of woven fabric from each TTS tribe makes outsiders and even native TTS people do not recognize the typical TTS woven fabric, therefore we need a system that can help facilitate the community in recognizing the type and motif of woven fabric. South Central Timor (TTS) is one of the districts that has a weaving culture and also produces woven cloth in East Nusa Tenggara. Keywords: Classifier Extraction Feature Hybrid Iris This is an open access article under the CC BY-SA license. Constructed on the outcome the planned method provided the most efficient effect as compared to the rest of the approach. In the third level, the error rate will be checked along with some statistical measures for final optimal results. Experimental based results provide for analysis according to the false receipt rate and false refusal amount. At last, perform matching process with decision based classifier for iris recognition with acceptance or rejection rates. Our model deploys on three types of datasets such as UBIRIS, CASIA, and MMU and gets optimal results for performing activity.
First level is having pre-processing steps which are necessary for the desired tasks. The proposed model for iris recognition with significant feature extraction was divided into three main levels. This paper presents an accurate framework for iris recognition system using hybrid algorithm in preprocess and feature extraction section. Iris recognition become one of the most accurate and reliable steadfast human biometric recognition system of the decad. The samples with the lower Kullback-Leibler values give a higher margin accuracy rate of 6.21% to 7.27%, thereby leading to better model adaptation for target transfer learning datasets and tasks
This proposed approach method yields two categories of the target samples with those with lower Kullback-Leibler values giving better accuracy, precision and recall. We experiment on three publicly available datasets and two ImageNet pre-trained models used in past studies for results comparisons. From comparing the various probability distance metrics, Kullback-Leibler is adopted to compare the samples from both domains. The extracted features are compared to the conflated low-level features of the target dataset to select a higher quality target dataset for improved pre-trained model performance and adaptation. This paper introduces the conflation of source domain low-level textural features extracted using the first layer of the pretrained model.
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These adaptation problems result from a lack of adequate transfer of traits from the source dataset this often leads to poor model performance resulting in trial and error in selecting the best performing pre-trained model. Adapting the target dataset for a pre-trained model is still challenging.
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