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Found 10 papers, 2 papers with code
Hand Biometrics in Digital Forensics
Biometric is a better solution to win over the problems encountered by digital forensics.
Finger Biometric Recognition With Feature Selection
The best identification accuracy of 98. 67%, and equal error rate (EER) of 4. 6% have been achieved using the subset of 25 features which are selected by the rank-based local FoBa algorithm.
Deep Ear Biometrics for Gender Classification
Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications.
Deep Neural Networks Fused with Textures for Image Classification
Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations.
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis
The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs.
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition.
Ranked #1 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Human-Object Interaction Detection
+3
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism.
Ranked #1 on
Image Classification
on Caltech-256
An attention-driven hierarchical multi-scale representation for visual recognition
Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content.
Spoofing Detection on Hand Images Using Quality Assessment
A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images.
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets.
Ranked #1 on
Fine-Grained Image Classification
on Food-101
