Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Abstract

Facial beauty prediction (FBP), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results as deep learning technology progressively develops. However, it is still limited by the complexity of the deep structure network in need of a large number of parameters and high dimensions, easily leading to a great consumption of time. To solve this problem, this paper proposes a fast training FBP method based on local feature fusion and broad learning system (BLS). Firstly, two-dimensional principal component analysis (2DPCA) is employed to reduce the dimension of the local texture image so as to lessen its redundancy. Secondly, local feature fusion method is adopted to extract more advanced features through avoiding the effects from unstable illumination, individual differences, and various postures. Finally, extensional feature eigenvectors are input to the broad learning network to train an efficient FBP model, which effectively shortens operational time and improve its preciseness. Extensive experiments with the proposed method on large scale Asian female beauty database (LSAFBD) can be conducted within 13.33s while sustaining an accuracy of 58.97%, impressively outstripping other state-of-the-art methods in training speed. INDEX TERMS Facial beauty prediction (FBP); local feature fusion; broad learning system (BLS);

Facial Beauty Prediction Based on Deep Learning: A Review

“…Zhai et al [43] discussed an innovative approach for facial beauty prediction (FBP) using artificial intelligence. The method involves a fast-training FBP model based on local feature fusion and a broad learning system (BLS).…”

Automatic Facial Aesthetic Prediction Based on Deep Learning with Loss Ensembles

“…However, geometric-feature-based techniques have limited performance due to the influence of facial-expression variation, and it demands a computational burden through landmark localizations. Psychological studies confirm that facial color, smoothness, and lightness are crucial for perceiving facial beauty [24][25][26]. Iyer et al [27] implemented conventional image descriptors for texture feature extraction and combined them with putative facial ratios to predict the attractiveness score.…”

Facial beauty prediction fusing transfer learning and broad learning system

“…Training of CNNs requires a lot of time and high-performance equipment, and a number of works have shown that this problem can be solved by way of BLS, a high-speed learning system without deep architecture (Chen et al 2019;Zhang et al 2019;Zhai et al 2020;Li et al 2021;Chang and Chun 2022). Zhai et al (2020) designed a new FBP architecture via BLS, by combining local feature fusion and 2D principal component analysis, training time was effectively reduced while maintaining good accuracy. Ranjana et al (2022) proposed Broad Learning and Hybrid Transfer Learning System for face mask detection, in which good results were obtained.…”

“…Zhang et al (2019) proposed a face recognition method based on BLS with feature block, demonstrating how face recognition with the help of BLS is not affected by the number of facial features in strong illumination and occlusion, and hold high accuracy. And a new FBP approach was designed by our group based on local feature fusion and BLS, in which the fused features of facial beauty are extracted by 2D dimensional principal component analysis, and these features are input into BLS for FBP, greatly reducing training time (Zhai et al 2020). Furthermore, a new method was designed for facial expression recognition in human robot interaction based on enhanced broad Siamese network (Li et al 2021), which efficiently decreased consumption of computing time and memory resources.…”