FV-MViT: Mobile Vision Transformer for Finger Vein Recognition.
Sensors (Basel)
; 24(4)2024 Feb 19.
Article
in En
| MEDLINE
| ID: mdl-38400488
ABSTRACT
In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer's self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electric Power Supplies
/
Extremities
Limits:
Humans
Language:
En
Journal:
Sensors (Basel)
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Switzerland