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

Zambrano, Jorge E.; Benalcazar, Daniel P.; Perez, Claudio A.; Bowyer, Kevin W.

doi:10.1109/access.2022.3166910

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…In the VGG-full test, we were able to minimize the required resize, allowing us to keep the width intact (240 pixels), while setting the height to either 32 pixels (for the 240 × 20 pixels images) or the original height of 40 pixels (for the 240 × 40 pixels images). Since the pretrained VGG-16 network requires RGB images, our grayscale NIR images are repeated in each of the three channels as part of the preprocessing [55]. Each test was repeated using 30 different train-test partitions to ensure results are statistically significant, as done in [4].…”

Section: Gender Classification Methodsmentioning

confidence: 99%

Impact of Occlusion Masks on Gender Classification from Iris Texture

Yáñez,

Tapia,

Perez

et al. 2024

IET Biometrics

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Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

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Section: Gender Classification Methodsmentioning

confidence: 99%

Impact of Occlusion Masks on Gender Classification from Iris Texture

Yáñez,

Tapia,

Perez

et al. 2024

IET Biometrics

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“…Convolutional Neural Networks (CNNs) consist of two main parts: the feature extractor or backbone and the classifier [1], [2], [3], [4]. The backbone extracts the most important features from an input image hierarchically, while the classifier corresponds to a fully connected neural network (NN) [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector

Pilataxi,

Zambrano,

Perez

et al. 2024

IEEE Access

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Designing Convolutional Neural Networks (CNNs) for a specific task requires not only Deep Learning expertise but also knowledge of the problem. The goal of Neuroevolution is to find CNN architectures automatically through evolution. The search time, however, is a critical problem in Neuroevolution since multiple CNNs must be trained in the evolutionary process. In this work, we propose a Neural Architecture Classifier (NAC) to avoid training architectures that would not have good performance, based on knowledge of previously trained architectures. The NAC evaluates each CNN using the CNN architecture encoding as the input. A genetic algorithm (GA) is used for evolution, and a search space with few restrictions; hence, CNN architectures can have any width, depth or shape as well as the suitable number of skip connections. We applied this methodology to solving pattern recognition problems on six well-known datasets: five subsets of the MNIST-Variations (MNIST-V) dataset and the CIFAR-10 dataset. Experiments demonstrated that integrating the NAC in the GA reduces the search time by up to 44% compared to conventional GAs. Additionally, the architectures found by evolution with our classifier achieved a better performance (4.6% on average) than those found by traditional evolution. Our results improved the stateof-the-art by 5.2% (0.33% error reduction) on the most difficult dataset of the MNIST-V (MRDBI), and an average of 4% (0.24% error reduction) on the MNIST-V datasets. Additionally, our results on CIFAR-10 are close to those of the state-of-the-art but with significantly reduced search time. The application of a classifier can be relevant and useful not only for genetic algorithms, but also for other evolutionary algorithms.INDEX TERMS Convolutional neural networks, genetic algorithms, neuroevolution, neural architecture classifier, search time.

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“…In the last decade, deep learning has enabled significant progress in a variety of applications including object detection [1,2], face recognition [3], iris recognition [4], genetic algorithms applied to CNNs [5,6], rock lithological classification [7], trademark image retrieval [8], and semantic segmentation [9], among others. Pedestrian detection is one of the key tasks in computer vision, for which several models have been developed in the past few years [10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Pedestrian Detection Model Using a New Classification Head for Domain Generalization

Schulz,

Perez

2023

Sensors

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Pedestrian detection based on deep learning methods have reached great success in the past few years with several possible real-world applications including autonomous driving, robotic navigation, and video surveillance. In this work, a new neural network two-stage pedestrian detector with a new custom classification head, adding the triplet loss function to the standard bounding box regression and classification losses, is presented. This aims to improve the domain generalization capabilities of existing pedestrian detectors, by explicitly maximizing inter-class distance and minimizing intra-class distance. Triplet loss is applied to the features generated by the region proposal network, aimed at clustering together pedestrian samples in the features space. We used Faster R-CNN and Cascade R-CNN with the HRNet backbone pre-trained on ImageNet, changing the standard classification head for Faster R-CNN, and changing one of the three heads for Cascade R-CNN. The best results were obtained using a progressive training pipeline, starting from a dataset that is further away from the target domain, and progressively fine-tuning on datasets closer to the target domain. We obtained state-of-the-art results, MR−2 of 9.9, 11.0, and 36.2 for the reasonable, small, and heavy subsets on the CityPersons benchmark with outstanding performance on the heavy subset, the most difficult one.

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

Cited by 7 publications

References 59 publications

Impact of Occlusion Masks on Gender Classification from Iris Texture

Impact of Occlusion Masks on Gender Classification from Iris Texture

Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector

Two-Stage Pedestrian Detection Model Using a New Classification Head for Domain Generalization

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