Comparing the Legendre Wavelet filter and the Gabor Wavelet filter For Feature Extraction based on Iris Recognition System

Danlami, Muktar; Jamel, Sapiee; Ramli, Sofia Najwa; Azahari, Siti Radhiah Megat

doi:10.1109/icoa49421.2020.9094465

Cited by 10 publications

(8 citation statements)

References 18 publications

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“…This system has been tested with the CASIA iris database and achieved an accurate recognition rate of 98.45%. Danlami et al (2020) suggested the use of Legendre wavelet filters with the most commonly used filters. The researchers tested these filters on three different datasets.…”

Section: Introductionmentioning

confidence: 99%

Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Aktürk¹,

Aydemir²,

Rashid³

2023

AIP

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Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer learning. In this study, a total of 1980 eye contour images of 96 different people were collected in order to solve the problem of recognizing people from their eye images. These collected data were classified in terms of person, age and gender. In the classification made for eye recognition, feature extraction was performed with 32 different transfer learning algorithms in the Python program and classified using the RandomForest algorithm for person estimation. According to the results of the research, 30 different classification algorithms were used, with the ResNet50 algorithm being the most successful, and the data were also classified in terms of age and gender. Thus, the highest success rates of 83.52%, 96.41% and 77.56% were obtained in person, age and gender classification, respectively. The study shows that people can be identified only by eye images obtained from a smartphone without using any special equipment, and even the characteristics of people such as age and gender can be determined. In addition, it has been concluded that eye images can be used in a more efficient and practical biometric recognition system than iris recognition.

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Section: Introductionmentioning

confidence: 99%

Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Aktürk¹,

Aydemir²,

Rashid³

2023

AIP

View full text Add to dashboard Cite

show abstract

“…Biometric information can be divided into physiological information determined by an individual’s genes and behavioural information gradually formed by an individual’s experiences. Physiological information includes fingerprints [ 1 ], iris patterns [ 2 ], facial features [ 3 ], and DNA [ 4 ], while behavioural information includes human handwriting [ 5 ], gait [ 6 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction

Han,

2023

Sensors

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As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial–temporal graph convolutional networks’ bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

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“…Mammography has been clinically proven effective for early detection. It reveals abnormal tissues in the breast before patients can feel or notice symptoms [1][2][3]. The abnormalities in the mammography images are masses, calcifications, lumps, architectural distortions, and so on.…”

Section: Introductionmentioning

confidence: 99%

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

Prakash¹,

Kumar²,

Ram³

et al. 2022

Computer Systems Science and Engineering

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Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid wavelet filter for accurate image enhancement. The main objective of enhancement is to produce quality images for detecting cancer sections in images. Image enhancement is the main step where the quality of the input image is improved to detect cancer masses. In this study, we use a combination of two filters, namely, Gabor and Legendre. The edges are detected using the Canny detector to smoothen the images. High-quality enhanced image is obtained through the Gabor-Legendre filter (GLFIL) process. Further image is used by classification algorithm. Animal migration optimization with neural network is implemented for classifying the image. The output is compared to existing filter techniques. Ultimately, the accuracy achieved by the proposed technique is 98%, which is higher than existing algorithms.

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Comparing the Legendre Wavelet filter and the Gabor Wavelet filter For Feature Extraction based on Iris Recognition System

Cited by 10 publications

References 18 publications

Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

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