Finger Vein Authentication Based on Wavelet Scattering Networks

Abstract

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades. Multiple biometrics such as fingerprint, palm, iris, palm vein and finger vein and other biometrics have been introduced. One of the challenges in biometrics is physical injury. Biometric of finger vein is of the biometrics least exposed to physical damage. Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate. This paper presents a novel method of scattering wavelet-based identity identification. Scattering wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural networks. What distinguishes this algorithm from other popular feature extraction methods such as deep learning methods, filter-based methods, statistical methods, etc., is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes, even when the image is subject to serious damage such as noise, angle changes or pixel location, this descriptor still generates feature vectors in a way that minimizes classifier error. This improves classification and authentication. The proposed method has been evaluated using two databases Finger Vein USM (FV-USM) and Homologous Multimodal biometrics Traits (SDUMLA-HMT). In addition to having reasonable computational complexity, it has recorded excellent identification rates in noise, rotation, and transmission challenges. At best, it has a 98.2% identification rate for the SDUMLA-HMT database and a 96.1% identification rate for the FV-USM database.

Improving recognition accuracy for facial expressions using scattering wavelet

“…Furthermore, that makes it stable for changes. However, since the wavelet transform computes convolutions with wavelet filters, the wavelet transform is unstable to changes [29,30]. To this end, a set of wavelet filters is needed to produce a descriptor with stable features against deformation, transmission, scaling, direction, and dilation [31][32][33].…”

Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

“…It does not recognize new attack variations. AIDS (Anomaly-based intrusion detection system) techniques for this issue are possible because it works on pro ling the appropriate behavior of attacks [34][35][36].…”

Face Recognition approach via Deep and Machine Learning

“…The deep wavelet scattering transform, known for its ability to capture multi-scale and invariant representations, has emerged as a promising approach for extracting robust features from facial images. By decomposing facial data into different frequency bands and orientations, the deep wavelet scattering transform effectively captures both local and global information, enabling enhanced face recognition accuracy [5]. The upcoming sections of this paper follow this organization: Section 2 examines related literature, Section 3 outlines the research background, Section 4 presents the methodology and the proposed approach in detail, Section 5 analyzes the experimental results, and finally, Section 6 concludes the paper along with discussing future work.…”