Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

Huang, Yu-Wen; Yang, Geng; Wang, Kuikui; Liu, Haiying; Yin, Yilong

doi:10.1007/s11390-021-1033-5

Cited by 7 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that MFDCSR and our previous work in literature [19] are different. Our previous work only extracted one feature as a feature descriptor, and MFDCSR extracted multiple features of the shape, wavelet, and PCA.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 75%

“…Our previous work used multiscale representation to deal with noise, and MFDCSR exploits the complementarity of features to improve the recognition performance. MFDCSR is a multiple feature learning method, and our previous work is a signal feature learning method, so MFDCSR and our previous work in literature [19] are different methods. As shown in Table 4, we can see that the feature-extraction time of MFDCSR is fast.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Deep cascade coding contains multiple levels, and each level is a SRC unit proposed in Section 3.1.2. Each level of deep cascade coding receives the input information consisted of the previous level and multifeature coding, and each level outputs the result that is the input of the next level[19], as shown in Figure2.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Multifeature Deep Cascaded Learning for PPG Biometric Recognition

Liu

Huang

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

Aiming at the problem that the traditional photoplethysmography (PPG) biometric recognition based on sparse representation is not robust to noise and intraclass variations when the sample size is small, we propose a PPG biometric recognition method based on multifeature deep cascaded sparse representation (MFDCSR). The method consists of multifeature signal coding and deep cascaded coding. The function of multifeature signal coding is to extract the shape, wavelet, and principal component analysis features of the PPG signal and to perform sparse representation. Deep cascaded coding is multilayer feature coding. Each layer combines multifeature signal coding with the result of the previous layer as input, and the output of each layer is the input of the next layer. The function of deep cascade coding is to learn the features of the PPG signal, layer by layer, and to output the category distribution vector of the PPG signal in the last layer. Experiments demonstrate that MFDCSR has better recognition performance than current methods for PPG biometric recognition.

show abstract

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 75%

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%