Iris recognition based on distance similarity and PCA

Sari, Yuslena; Alkaff, Muhammad; Pramunendar, Ricardus Anggi

doi:10.1063/1.5042900

Cited by 10 publications

(10 citation statements)

References 16 publications

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“…Matching an image with another image can be done by measuring the level of similarity that exists between the two images or by measuring how close they are to each other [26]. The measure of similarity or distance is the core component used by distance-based grouping to group similar data points into the same cluster, while in different or distant algorithms, data points are placed into different clusters [27].…”

Section: Resultsmentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

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Section: Resultsmentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

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“…The Euclidean distance (ED) method compares the minimum distance of the test image with the training image database [12], [22]. The ED of the two vectors x and y is calculated by (19) [23]:…”

Section: Classification Using Multi Distance Measure 231 Euclidean Di...mentioning

confidence: 99%

“…Canberra distance (CD) is used to get the distance from a pair of points where the data is original and in a vector space. CD provides output in the form of actual (true) and false (false) values as shown in (20) [23]- [25].…”

Section: Canberra Distancementioning

confidence: 99%

Facial recognition using multi edge detection and distance measure

Intan

Nurdin

Pangerang

2023

IJ-AI

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Face recognition provides broad access to several public devices, so it is essential in the midst of today's technology boom. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.

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“…In the matching process, the distance of city blocks was used to determine the absolute difference among two vectors, as in equation (19). The feature vector was obtained by previously mentioned techniques in the proposed system [18].…”

Section: -The Matchingmentioning

confidence: 99%

Iris Identification Based on the Fusion of Multiple Methods

Hashim

Al-Khalidy

2021

eijs

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Iris recognition occupies an important rank among the biometric types of approaches as a result of its accuracy and efficiency. The aim of this paper is to suggest a developed system for iris identification based on the fusion of scale invariant feature transforms (SIFT) along with local binary patterns of features extraction. Several steps have been applied. Firstly, any image type was converted to grayscale. Secondly, localization of the iris was achieved using circular Hough transform. Thirdly, the normalization to convert the polar value to Cartesian using Daugman’s rubber sheet models, followed by histogram equalization to enhance the iris region. Finally, the features were extracted by utilizing the scale invariant feature transformation and local binary pattern. Some sigma and threshold values were used for feature extraction, which achieved the highest rate of recognition. The programming was implemented by using MATLAB 2013. The matching was performed by applying the city block distance. The iris recognition system was built with the use of iris images for 30 individuals in the CASIA v4. 0 database. Every individual has 20 captures for left and right, with a total of 600 pictures. The main findings showed that the values of recognition rates in the proposed system are 98.67% for left eyes and 96.66% for right eyes, among thirty subjects.

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Iris recognition based on distance similarity and PCA

Cited by 10 publications

References 16 publications

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Facial recognition using multi edge detection and distance measure

Iris Identification Based on the Fusion of Multiple Methods

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