Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Szymkowski, Maciej; Jasiński, Piotr; Saeed, Khalid

doi:10.1007/s11334-021-00392-9

Cited by 18 publications

(3 citation statements)

References 18 publications

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“…Mean accuracy of iris segmentation of sample images using CNN is 93.9% and by using SVM is reached to 71.6%. Szymkowski et al, [13] proposed a discrete fast Fourier transform (DFFT) system for iris-based human identification recognition using principal component analysis technique. K-nearest neighbors, support vector machines (SVM), and artificial neural networks were the three techniques employed for categorization.…”

Section: Swati and Deepakmentioning

confidence: 99%

Efficient Iris Image Recognition System Based on Machine Learning Approach

2023

IJCCCE

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Iris identification is a well‐known technology used to detect striking biometric identification techniques for recognizing human beings based on physical behavior. The texture of the iris is essential and its anatomy varies from individual to individual. Humans have distinctive physical characteristics that never change. This has resulted in a considerable advancement in the field of iris identification, which inherits the random variation of the data and is often a dependable technological area. This research proposes three algorithms to examine the classifications in machine learning approaches using feature extraction for the iris image. The applied recognition system used many methods to enhance the input images for iris recognition using the Multimedia University (MMU) database. Linear Discriminant Analysis (LDA) feature extraction method is applied as an input of three algorithms of machine learning approaches that are OneR, J48, and JRip classifiers. The result indicates that the OneR classifier with LDA achieves the highest performance with 94.387 % accuracy, while J48 and JRip reached to 90.151% and 86.885% respectively. Index Terms— Iris recognition, LDA, OneR, J48, JRip.

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Section: Swati and Deepakmentioning

confidence: 99%

Efficient Iris Image Recognition System Based on Machine Learning Approach

2023

IJCCCE

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“…The issue of computational feasibility, which can be provided in real time, is typically more significant than the lack of adaptability. The DCT can be obtained by rearranging the elements of the input vectors to construct a Fast Fourier Transform (FFT) algorithm [17].…”

Section: Datasetmentioning

confidence: 99%

Securing the Biometric through ECG using Machine Learning Techniques

Gupta,

Parihar,

Rastogi

et al. 2023

IJRITCC

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In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG). The main reason behind this is the certain uniqueness in the ECG signals of the individual. In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques. The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3. The work has shown how machine learning can be used to classify the ECG signal.

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“…The reliability of iris is high compared to other biometric which refers to how dependable it is for recognition purpose [9]. While the uniqueness of the iris is also high which means the iris is distinct from each other [10] make it extensively investigated for accurate and personal identification [11].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Iris Recognition Technique using CNN and Vision Transformer

Samihah Abdul Latif,

Khairul Azami Sidek,

Aisha Hassan Abdalla Hashim

2023

ARASET

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The usage of biometric identification has increased in recent years, with numerous public and commercial organizations incorporating biometric technologies into their infrastructures. One of the technologies is iris recognition which has been used as a biometric recognition compared to other modalities to combat identity abuse due to its ability to eliminate risk of collisions or false matches even when comparing large populations. The use of CNN is proven to provide high accuracy; however, this technology involves the need for a large dataset and higher computational cost. Therefore, this study uses a combined model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) in identifying and verifying an iris image. By using the proposed learning rate, it proves that the novel hybrid model is capable to achieve up to 93.66% accuracy in recognizing iris images. The cross-entropy loss function was implemented to reduce the loss and it was able to predict the class label more correctly. In addition, the model was thoroughly tested on three publicly available iris databases, achieving satisfactory iris recognition results. Furthermore, this model has the potential to be used in other biometrics such as face and retina recognitions.

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Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Cited by 18 publications

References 18 publications

Efficient Iris Image Recognition System Based on Machine Learning Approach

Efficient Iris Image Recognition System Based on Machine Learning Approach

Securing the Biometric through ECG using Machine Learning Techniques

An Efficient Iris Recognition Technique using CNN and Vision Transformer

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