Facial beauty prediction via deep cascaded forest

Zhai, Yongjie; Lv, Peilun; Deng, Wenbo; Xie, Xueyin; Yu, Cuilin; Gan, Junying; Zeng, Junying; Ying, Zilu; Labati, Ruggero Donida; Piuri, Vincenzo; Scotti, Fabio

doi:10.1504/ijhpsa.2020.111559

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-grained scanning similar to sliding window technology, which is usually used to process raw data for data argumentation for better training. For instance, Zhai et al obtain image features through grained scanning to predict the level of facial appearance [ 38 ], and Liu et al introduce a multi-grained scanning method to enrich features for credit scoring [ 39 ]. The current study replaces the grained scanning by human-crafted feature extraction, which significantly reduces the dimension of the input for cascading forest, and speeds up model converging.…”

Section: Discussionmentioning

confidence: 99%

Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals

Fang

Liu

2022

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Bio-signal based hand motion recognition plays a critical role in the tasks of human-machine interaction, such as the natural control of multifunctional prostheses. Although a large number of classification technologies have been taken to improve the motion recognition accuracy, it is still a challenge to achieve acceptable performance for multiple modality input. This study proposes a multi-modality deep forest (MMDF) framework to identify hand motions, in which surface electromyographic signals (sEMG) and acceleration signals (ACC) are fused at the input level. The proposed MMDF framework constitutes of three main stages, sEMG and ACC feature extraction, feature dimension reduction, and a cascade structure deep forest for classification. A public database “Ninapro DB7” is used to evaluate the performance of the proposed framework, and the experimental results show that it can achieve a significantly higher accuracy than that of competitors. Besides, our experimental results also show that MMDF outperforms other traditional classifiers with the input of the single modality of sEMG signals. In sum, this study verifies that ACC signals can be an excellent supplementary for sEMG, and MMDF is a plausible solution to fuse mulit-modality bio-signals for human motion recognition.

show abstract

Section: Discussionmentioning

confidence: 99%