ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance

2021 ◽  
Vol 66 (2) ◽  
pp. 167-180
Author(s):  
Swati D. Shirke ◽  
Cherukuri Rajabhushnam
Keyword(s):  
Iris Recognition ◽  
Automatic Segmentation ◽  
Rejection Rate ◽  
Monarch Butterfly ◽  
False Acceptance Rate ◽  
Rubber Sheet ◽  
Monarch Butterfly Optimization ◽  
Scattering Transform ◽  
Tetrolet Transform ◽  
Visual Imaging

Abstract Iris Recognition at-a Distance (IAAD) is a major challenge for researchers due to the defects associated with the visual imaging and poor image quality in dynamic environments, which imposed bad impacts on the accuracy of recognition. Thus, in order to enable the effective IAAD, this paper proposes a new method, named, Chronological Monarch Butterfly Optimization (Chronological MBO)-enabled Neural Network (NN). The recognition of iris using NN is trained with the proposed Chronological MBO, which is developed through the combination of Chronological theory in Monarch Butterfly Optimization (MBO). The recognition becomes effective with the automatic segmentation and the normalization of iris image on the basis of Hough Transform (HT) and Daugman’s rubber sheet model followed with the process of feature extraction with the developed ScatT-LOOP descriptor, which is the integration of scattering transform (ST), Local Optimal Oriented Pattern (LOOP) descriptor, and Tetrolet transform (TT). The developed ScatT-LOOP descriptor extracts the texture as well as the orientation details of image for effective recognition. The analysis is evaluated with the CASIA Iris dataset with respect to the evaluation metrics, accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The proposed method has the accuracy, FRR, and FAR of 0.97, 0.005, and 0.005, respectively. The experimental results proved that the proposed method is effective than the existing methods of iris recognition.

Local gradient pattern and deep learning-based approach for the iris recognition at-a-distance

2021 ◽  
Vol 25 (1) ◽  
pp. 49-64
Author(s):  
Swati D. Shirke ◽  
C. Rajabhushnam
Keyword(s):  
Deep Learning ◽  
Iris Recognition ◽  
Learning Strategy ◽  
Rejection Rate ◽  
False Acceptance Rate ◽  
Rubber Sheet ◽  
Scattering Transform ◽  
Gradient Based ◽  
Local Gradient ◽  
Human Eyes

One of the biometric techniques utilized to predict the human is based on the iris. The recognition of iris is performed by discovering an individual without human intervention utilizing the iris of human eyes. Iris offers distinct information about the person. This research presents deep learning strategy for performing iris recognition. Primarily, image is pre-processed to obtain exact region iris. Then, region of iris is extracted using Hough Transform, followed by segmentation and normalization of iris region using Daugman’s rubber sheet model. Once segmentation is performed, the features are generated with ScaT-LOOP that is the combination of Scattering Transform (ST), Tetrolet transforms (TT), Local Gradient Pattern (LGP) and Local Optimal Oriented Pattern (LOOP). Finally, steepest gradient-based Deep Belief Network (DBN) is utilized for recognizing the iris. The performance of iris recognition using the DBN classifier is computed based on accuracy, False Rejection Rate (FRR), and False Acceptance Rate (FAR). The proposed method achieves maximum accuracy of 97.96%, minimal FAR of 0.493%, and minimal FRR of 0.48% that indicates its superiority.

scholarly journals Iris Recognition from an Image at Lengthy Distance by using Deep Belief Neural Network (DBN)

2019 ◽  
Vol 8 (5S3) ◽  
pp. 523-532
Keyword(s):  
Neural Network ◽  
Iris Recognition ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
False Acceptance Rate ◽  
Rubber Sheet ◽  
The Individual ◽  
Scattering Transforms ◽  
Local Gradient ◽  
Precise Area

Now a days, biometric is a one of the best method which is used for the detection of person is iris recognition. A large portion of different frameworks are equally introduces for individual ID like as distinguishing proof cards or tokens, mystery codes, passwords, and so on. Yet, the issues of these sort of frameworks are, the mystery codes and passwords can be split, the recognizable proof cards can be harmed. Subsequently the successful strategy for the individual recognizable proof is vital. Iris gives the unmistakable data about an individual. Iris recognition is the process of identifying persons automatically using their iris. Iris provides the distinctive information about a person. This paper exhibits the deep learning-based methodology for the iris acknowledgment. Firstly, the picture is pre-handled to get the precise area of the iris. From that point onward, iris locale is extricated utilizing Hough Transform, which is pursued with the division and standardization of the iris area utilizing the Daugman's Rubber sheet model. When the division is played out, the features are separated by utilizing the Local Gradient Pattern (LGP) and ScaT-LOOP that is the mixture of Scattering transforms (ST), Tetrolet transforms (TT), and Local Optimal Oriented Pattern (LOOP) descriptors. At last, steepest slope based Deep Belief Neural Network (DBN) is used for the iris acknowledgment. The exhibition of iris acknowledgment utilizing the DBN classifier is assessed regarding precision, False Rejection Rate (FRR) and False Acceptance Rate (FAR). The proposed iris acknowledgment strategy accomplishes the most extreme precision of 97.96%, negligible FAR of 0.493%, and insignificant FRR of 0.48% that shows its predominance.

scholarly journals Biometric Iris Recognition of Person from an Image at Long Distance using Chronological Monarch Butterfly Optimization based Deep Belief Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1975-1983
Keyword(s):  
Feature Extraction ◽  
Hough Transform ◽  
Iris Recognition ◽  
Median Filter ◽  
Monarch Butterfly ◽  
Deep Belief Network ◽  
Long Distance ◽  
Belief Network ◽  
Monarch Butterfly Optimization

Now days, for the identification of personal information of a person, biometrics is mostly used. Also for the personal identification, the recognition of eye based biometric feature extraction is the most powerful tool. The biometric is an important identity to identify the individual. But in real time it is quite difficult to capture the better quality of iris images. The images obtained are more degraded due to the lack of texture, blur. In this paper, more convenient method is presented for extracting the features of biometric images. The method Iris Recognition at-a Distance (IAAD) is used to extract the iris features of biometric image and to enhance the quality of an image in a biometric system. The Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) is proposed for iris recognition to get better accuracy. The Monarch Butterfly Optimization algorithm is used to arrange the Chronological assumption of an iris image. Also, the Hough Transform algorithm is used for detection of iris circle and edge. The scaT T loop descriptor and the Local Gradient Pattern (LGP) are used for feature extraction, which is fed to the Chronological MBO-based DBN for iris recognition that enhances the accuracy. The Daugman’s rubber sheet model, median filter and trained neural network are used for normalization and segmentation. The UBIRIS.v1 database is used to take an iris recognition images and MATLAB is used for programming of for reading the iris images and for performing the Hough transform operations. The iris recognition at a distance 4 to 8 meter is done with the help of simulation result. The performance is analyzed based on the metrics, like False Acceptance Rate (FAR), accuracy, and False Rejection Rate (FRR) with the value of 0.4847%, 96.078%, and 0.4745%

scholarly journals Iris Recognition at-a-Distance by Means of Chronological MBO-Based DBN

2019 ◽  
Vol 8 (12) ◽  
pp. 4540-4552
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Monarch Butterfly ◽  
Recognition System ◽  
Monarch Butterfly Optimization ◽  
Iris Image ◽  
Safety Reason ◽  
Local Gradient ◽  
Detection Schemes ◽  
Actual Method

Now a days, Iris recognition is wieldy used for the identification of person. The superior bit of 1 countries exploits biometric system for safety reason with the conclusion goal that in runway boarding, custom freedom, gathering passage, etc. The Iris detection at-a-Distance (IAAD) framework is generally used to identify the person in most of the applications. In this system, different features of iris image are extracted in addition enhances the superiority of iris image. Over the span of the most recent ages there consume raised various structures to design and finish iris affirmation systems which works at longer separation going from one meter to sixty meter. Because of such long scope of iris detection schemes in addition iris attainment scheme provides for the best applications to the client. Therefore, It is necessary to design an effective algorithm for IAAD is necessary. In this article, an actual method for iris recognition is presents. A Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) technique is anticipated for iris detection.This technique algorithm is the combination of Chronological theory with the Monarch Butterfly Optimization. It is utilized to mastermind the sequential presumption of an iris picture. Additionally, the Hough Transform calculation is utilized for discovery of iris circle and edge. To enhance the accuracy of anticipated iris recognition system ScatT-Loop descriptor and the Local Gradient Pattern (LGP) are fed to the Chronological MBO-based DBN algorithm and these are castoff to abstract the dissimilar features of an iris picture. The dataset used for these tactices are UBIRIS.v1 For the normalization and segmentation of an iris image is done by by means of Dougman's rubber sheet model. This system is established on MATLAB for executing the Hough transform procedures also for reading the iris images. The simulation results shows that this system successfully recognize the iris at a distance 4 to 8 meter. Different performance parameters like as FAR accuracy, too FRR shows better results in this anticipated work.

TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES

2021 ◽  
pp. 2150041
Author(s):  
G. MERCY BAI ◽  
P. VENKADESH
Keyword(s):  
Support Vector Machine ◽  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Monarch Butterfly ◽  
Support Vector ◽  
Training Time ◽  
Monarch Butterfly Optimization

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.

COMPARISON OF COLOR AND TEXTURE FOR IRIS RECOGNITION

2012 ◽  
Vol 26 (03) ◽  
pp. 1256007 ◽  
Author(s):  
LENINA BIRGALE ◽  
MANESH KOKARE
Keyword(s):  
Iris Recognition ◽  
Feature Vector ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Computational Time ◽  
False Acceptance Rate ◽  
Recognition Systems ◽  
Normalization Process ◽  
False Acceptance ◽  
False Rejection

This paper proposes the utility of texture and color for iris recognition systems. It contributes for improvement of system accuracy with reduced feature vector size of just 1 × 3 and reduction of false acceptance rate (FAR) and false rejection rate (FRR). It avoids the iris normalization process used traditionally in iris recognition systems. Proposed method is compared with the existing methods. Experimental results indicate that the proposed method using only color achieves 99.9993 accuracy, 0.0160 FAR, and 0.0813 FRR. Computational time efficiency achieved is of 947.7 ms.

scholarly journals Acute Lymphoblastic Leukemia Classification with Blood Smear Microscopic Images Using Taylor-MBO based SVM

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 357-366
Author(s):  
Nashwan Jasim Hussein
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Monarch Butterfly ◽  
Support Vector ◽  
Training Time ◽  
Monarch Butterfly Optimization ◽  
Abnormal Growth

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Hence, an effective leukemia detection approach is designed using the proposed Taylor-Monarch Butterfly Optimization based Support Vector Machine (Taylor-MBO based SVM). However, the proposed Taylor-MBO is designed by the integration of Taylor series and Monarch Butterfly Optimization (MBO), respectively. The sparking process is designed to perform automatic segmentation of blood smear image by estimating optimal threshold values. By extracting the features, such as texture features, statistical and grid-based features from the segmented smear image, the performance of classification is increased with less training time However, the proposed Taylor-MBO based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity with the values of 94.5751%, 95.526%, and 94.570%, respectively.

SISTEM PENGENALAN MANUSIA MELALUI KEUNIKAN FISIOLOGIS SELAPUT PELANGI MATA DENGAN MENGGUNAKAN FILTER LOG GABOR

KONVERGENSI ◽  
2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Bima Agung Pratama ◽  
Fajar Astuti Hermawati
Keyword(s):  
Iris Recognition ◽  
Hamming Distance ◽  
Rubber Sheet ◽  
Log Gabor

Penelitian ini mengajukan sebuah sistem pengenalan manusia melalui karakteristik pola fisiologis selaput pelangi (iris) matanya. Pengenalan selaput pelangi mata (iris recognition) merupakan suatu teknologi pengolahan citra yang digunakan untuk mendeteksi dan menampilkan selaput pelangi (iris) pada alat indera mata manusia saat kelopak mata terbuka. Terdapat beberapa tahap dalam proses pengenalan menggunakan pola iris mata manusia. Langkah pertama adalah melakukan proses segmentasi untuk mendapatkan daerah selaput pelangi (iris) mata yang berbentuk melingkat dengan menggunakan metode operator integro-diferensial. Selanjutnya dilakukan proses normalisasi hasil segmentasi menjadi bentuk polar dengan menerapkan metode metode Daughman’s rubber sheet model. Setelah itu diterapkan proses ekstraksi fitur atau pola dari citra ternormalisasi menggunakan filter Log-Gabor. Pencocokan untuk mengukur kesamaan antara pola iris mata manusia dengan pola-pola dalam basisdata sistem dilakukan menggunakan Hamming distance. Dalam percobaan pengenalan individu menggunakan basisdata iris mata MMU diperoleh akurasi sebesar 98%. Kata Kunci: Pengenalan selaput pelangi, Pengenalan iris mata, Filter log-Gabor, Segmentasi citra, Sistem biometrik

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