• Corpus ID: 236924626

Multi-Branch with Attention Network for Hand-Based Person Recognition

  title={Multi-Branch with Attention Network for Hand-Based Person Recognition},
  author={Nathanael L. Baisa and Bryan M. Williams and Hossein Rahmani and Plamen P. Angelov and Sue Black},
In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in cases of serious crime such as sexual abuse. Our proposed method, Multi-Branch with Attention Network (MBA-Net), incorporates both channel and spatial attention modules in branches in addition to a global (without attention) branch to capture global structural information for discriminative feature learning. The… 

Figures and Tables from this paper

Local-Aware Global Attention Network for Person Re-Identification

A compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images is proposed and consistently outperforms existing state-of-the-art methods.



Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning

The proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features.

Global Self-Attention Networks for Image Recognition

A new global self-attention module, referred to as the GSA module, which is efficient enough to serve as the backbone component of a deep network, and introduces new standalone global attention-based deep networks that use GSA modules instead of convolutions to model pixel interactions.

Bag of Tricks and a Strong Baseline for Deep Person Re-Identification

A simple and efficient baseline for person re-identification with deep neural networks by combining effective training tricks together, which achieves 94.5% rank-1 and 85.9% mAP on Market1501 with only using global features.

Scalable Person Re-identification: A Benchmark

A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.

Relation-Aware Global Attention for Person Re-Identification

This work proposes an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning and proposes to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions together to learn the attention with a shallow convolutional model.

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

  • M. Afifi
  • Computer Science
    Multimedia Tools and Applications
  • 2019
A two-stream convolutional neural network which accepts hand images as input and predicts gender information from these hand images is designed, and the dorsal side of human hands is found to have effective distinctive features similar to, if not better than, those available in the palmar side ofhuman hand images.

ABD-Net: Attentive but Diverse Person Re-Identification

An Attentive but Diverse Network (ABD-Net), which seamlessly integrates attention modules and diversity regularizations throughout the entire network to learn features that are representative, robust, and more discriminative.

HandNet: Identification Based on Hand Images Using Deep Learning Methods

The proposed algorithm was tested on different datasets and the experimental results showed that high accuracy can be obtained from the fusion of features, showing that the hand image is a strong biometric for verification and identification.

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

A multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions and fusion leads to improved performance over single modality approaches.

Deep Residual Learning for Image Recognition

This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.