Finger-vein recognition based on parametric-oriented corrections

Hsia²,

2021

IEEE Access

Self Cite

Section: Comparison With Related Workmentioning

confidence: 88%

Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network

Hsia²,

2021

IEEE Access

Self Cite

Computers, Materials &Amp; Continua

“…Convolutional neural networks are modeled to classify finger vein images [31]. Convolutional neural networks are one of the deep learning methods that have recently attracted many researchers' attention.…”

Section: Research Backgroundmentioning

confidence: 99%

Finger Vein Authentication Based on Wavelet Scattering Networks

Rehman¹,

Harouni²,

Omidiravesh³

et al. 2022

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.

“…As a result, most of these approaches can only be applied to frontal face emotion recognition with short distances. With the rapid development of GPU and the expansion of expression datasets, these engineered learning methods have been gradually replaced by deep learning algorithms dominated by convolutional neural networks (CNN) when processing large and complex data [8][9][10]. Deep learning approaches for facial expression approaches use the powerful feature detectors of deep CNN to extract facial expression features, and achieve high performance by deepening the layers of the network and developing effective learning mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Efficient Recognition of Facial Expression with Lightweight Octave Convolutional Neural Network

Lai¹,

Chen²,

Li³

et al. 2022

jist

In various social activities, people usually focus on the other person’s facial expression, which is an important element in interpersonal communication. The facial expression typically reflects a person’s current mood and conveys emotional information. Perceiving the facial expressions of people in images through cameras has always been a popular research topic. Previous studies have classified facial expressions into different categories, such as happy, sad, fear, angry, calm, disgust and surprise, and have identified them using image processing methods. However, traditional image processing methods have a low detection efficiency and low recognition accuracy due to variation of perspectives. As a result, most of them can only be applied to the front face and short distance situations. In this paper, we propose a lightweight deep learning framework for facial expression recognition using Octave convolutional neural network (FerOctNet). FerOctNet including multi-scale convolutional processing and residual learning is able to obtain multi-scale features with enriched representation ability by integrating the deep level features with rich semantic information with shallow details of the features. Compared with other deep learning networks, not only does the proposed method have a good recognition rate, but also contains fewer parameters in the network.