Facial Beauty Prediction via Local Feature Fusion and Broad Learning System
| Autor: | Qin Chuanbo, Ke Qirui, Wenlve Zhou, Ruggero Donida Labati, Vincenzo Piuri, Yikui Zhai, Gan Junying, Fabio Scotti, Yu Cuilin |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Artificial neural network Computer science business.industry Deep learning 020208 electrical & electronic engineering Feature extraction General Engineering Pattern recognition 02 engineering and technology broad learning system (BLS) Facial recognition system Domain (software engineering) Facial beauty prediction (FBP) Dimension (vector space) local feature fusion Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 218444-218457 (2020) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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