A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system.Methods: Forty nine healthy, disease free, 3 months ± 6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9 and 12 months of age using a specialized camera made for human iris scanner. iGoat software was used for matching the same individual goats at 3, 6, 9 and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results:The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9 and 12 months of ages were recorded as 81.63%, 90.24%, 44.44% and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17% and 87.76% for identification of goat at 3, 6, 9 and 12 months of ages, respectively after training the model.

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“…In [31,32]. It was also noted that the iris images of some goats could not satisfy the threshold value at matching using template.…”

Section: Accuracy Of Eye Image Matching Of Goats Using Resnet152v2 De...mentioning

confidence: 99%

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

Laishram¹,

Mandal²,

Haldar³

et al. 2023

Anim Biosci

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“…The experiments for O-GAs and RS were performed on the MRDBI dataset because it is the most difficult among the MNIST-V sets. We ran each experiment five times, and we reported the mean and lowest error and performed statistical tests to compare the results as in [57], [63], [106]- [108].…”

Section: A Ordinary Genetic Algorithms and Random Searchmentioning

confidence: 99%

Two-Level Genetic Algorithm for Evolving Convolutional Neural Networks for Pattern Recognition

Montecino

Perez²,

Bowyer

2021

IEEE Access

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The aim of Neuroevolution is to find neural networks and convolutional neural network (CNN) architectures automatically through evolutionary algorithms. A crucial problem in neuroevolution is search time, since multiple CNNs must be trained during evolution. This problem has led to fitness acceleration approaches, generating a trade-off between time and fitness fidelity. Also, since search spaces for this problem usually include only a few parameters, this increases the human bias in the search. In this work, we propose a novel two-level genetic algorithm (GA) for addressing the fidelity-time trade-off problem for the fitness computation in CNNs. The first level evaluates many individuals quickly, and the second evaluates only those with the best results more finely. We also propose a search space with few restrictions, and an encoding with unexpressed genes to facilitate the crossover operation. This search space allows CNN architectures to have any sizes, shapes, and skip-connections among nodes. The two-level GA was applied to the pattern recognition problem on seven datasets, five MNIST-Variants, Fashion-MNIST, and CIFAR-10, achieving significantly better results than all those previously published. Our results show an improvement of 39.89% (4.2% error reduction) on the most complex dataset of MNIST (MRDBI), and on average 30.52% (1.35% error reduction) on all the five datasets. Furthermore, we show that our algorithm performed as well as precise-training GA, but took only the time of a fast-training GA. These results can be relevant and useful not only for image classification problems but also for GA-related problems.

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“…One approach to solve these limitations has been to extract iris features using publicly-available models, trained for natural image classification [25]- [27], and face recognition [28]. Most of these methods use the rubber sheet model as the input, and apply a mask to eliminate non-iris features, such as eyelids, eyelashes, and reflection artifacts [4], [16], [29], [30]. This approach has been very successful, however, there are still some limitations, including fine-tuning for each new iris dataset.…”

Section: Introductionmentioning

confidence: 99%

Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

et al. 2022

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A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks

Cited by 16 publications

References 46 publications

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

Two-Level Genetic Algorithm for Evolving Convolutional Neural Networks for Pattern Recognition

Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

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