Title: Facial beauty prediction via deep cascaded forest

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

Addresses: Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Department of Intelligence Manufacturing, Wuyi University, 529020, Jiangmen, China ' Departimento di Information, Universita degli Studi di Milano, 20133 Crema, Italy ' Departimento di Information, Universita degli Studi di Milano, 20133 Crema, Italy ' Departimento di Information, Universita degli Studi di Milano, 20133 Crema, Italy

Abstract: Facial beauty prediction (FBP), which is a prediction based on the classification of human facial beauty, has been applied in some social platforms and entertainment software. However, among the various approaches to FBP, methods based convolutional network is too complicated, and traditional methods cannot achieve the desired performance. In this paper, we propose a method for FBP via deep cascade forest. This method uses multi-grained scanning to obtain the features of the image, and uses multiple random forests to enhance the features. Then multiple classifiers to form a new classifier, which is used for predicting the acquired features to complete the FBP task. This method shows the advantages of feature extraction and relatively high prediction accuracy in 10,000 facial beauty datasets (10TFBD). And we are optimised for the cascade forest part and further improved the prediction accuracy. Our experiments demonstrate the effectiveness of FBP tasks.

Keywords: FBP; facial beauty prediction; deep forest; multi-grained scanning; cascade forest; random forest; representation learning.

DOI: 10.1504/IJHPSA.2020.111559

International Journal of High Performance Systems Architecture, 2020 Vol.9 No.2/3, pp.97 - 106

Received: 17 Feb 2020
Accepted: 21 Apr 2020
Published online: 01 Dec 2020 *

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