Study of a committee of neural networks for biometric hand-geometry recognition

• URL: http://arxiv.org/abs/2204.03935v1
• Date: Fri, 8 Apr 2022 08:56:42 GMT
• Title: Study of a committee of neural networks for biometric hand-geometry recognition
• Authors: Marcos Faundez-Zanuy
• Abstract summary: We show that a committee of nets can improve the recognition rates when compared with a multi-start algo-rithm that just picks up the neural net which offers the best performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared with a multi-start initialization algo-rithm that just picks up the neural net which offers the best performance. On the other hand, we found that there is no strong correlation between identifi-cation and verification applications using the same classifier.

Related papers

• Understanding attention-based encoder-decoder networks: a case study with chess scoresheet recognition [0.36832029288386137]
  We study encoder-decoder recurrent neural networks with attention mechanisms for the task of reading handwritten chess scoresheets. We characterize the task in terms of three subtasks, namely input-output alignment, sequential pattern recognition, and handwriting recognition. We argue that such knowledge might help one to better balance factors to properly train a network.
  arXiv  Detail & Related papers  (2024-04-23T16:23:18Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Multilayer Multiset Neuronal Networks -- MMNNs [55.2480439325792]
  The present work describes multilayer multiset neuronal networks incorporating two or more layers of coincidence similarity neurons. The work also explores the utilization of counter-prototype points, which are assigned to the image regions to be avoided.
  arXiv  Detail & Related papers  (2023-08-28T12:55:13Z)
• Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection [54.83707803301847]
  The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density.
  arXiv  Detail & Related papers  (2022-06-04T03:17:15Z)
• Biometric identification by means of hand geometry and a neural net classifier [0.0]
  We have acquired a database of 22 people using a conventional document scanner. The experimental section consists of a study about the discrimination capability of different extracted features.
  arXiv  Detail & Related papers  (2022-04-08T08:40:45Z)
• Similarity and Matching of Neural Network Representations [0.0]
  We employ a toolset -- dubbed Dr. Frankenstein -- to analyse the similarity of representations in deep neural networks. We aim to match the activations on given layers of two trained neural networks by joining them with a stitching layer.
  arXiv  Detail & Related papers  (2021-10-27T17:59:46Z)
• Learning Structures for Deep Neural Networks [99.8331363309895]
  We propose to adopt the efficient coding principle, rooted in information theory and developed in computational neuroscience. We show that sparse coding can effectively maximize the entropy of the output signals. Our experiments on a public image classification dataset demonstrate that using the structure learned from scratch by our proposed algorithm, one can achieve a classification accuracy comparable to the best expert-designed structure.
  arXiv  Detail & Related papers  (2021-05-27T12:27:24Z)
• Provably Training Neural Network Classifiers under Fairness Constraints [70.64045590577318]
  We show that overparametrized neural networks could meet the constraints. Key ingredient of building a fair neural network classifier is establishing no-regret analysis for neural networks.
  arXiv  Detail & Related papers  (2020-12-30T18:46:50Z)
• A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism [2.3204178451683264]
  We introduce a new attention-based neural network architecture called Audio-Based Convolutional Neural Network (CAB-CNN) The algorithm uses a newly designed architecture consisting of a list of simple classifiers and an attention mechanism as a selector. Compared to the state-of-the-art algorithms, our algorithm achieves more than 10% improvements on all selected test scores.
  arXiv  Detail & Related papers  (2020-06-14T21:29:44Z)
• The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile [55.41644538483948]
  A U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.
  arXiv  Detail & Related papers  (2020-05-25T13:51:03Z)
• A copula-based visualization technique for a neural network [0.0]
  Interpretability of machine learning is defined as the extent to which humans can comprehend the reason of a decision. We propose a new algorithm that reveals which feature values the trained neural network considers important.
  arXiv  Detail & Related papers  (2020-03-27T10:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.