SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1,~5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.Comment: 6 pages, 14 figures, conference pape

Miniaturizable Phase-Sensitive Amplifier Based on Vector Dual-Pump Structure for Phase Regeneration of PDM Signal

Phase sensitive amplification is indispensable in promoting applications such as all-optical regenerators, quantum communications, all-optical analog-to-digital conversion, and long-distance communications. In this article, we proposed a vector dual-pump nondegenerate phase-sensitive amplification scheme based on ultra-silicon-rich nitride (Si7N3) waveguide, and theoretically verified its capability for all-optical regeneration of phase-encoded polarization-division multiplexing (PDM) signal without the need for complex polarization diversity structures. We achieved a gain extinction ratio (GER) of ∼37.5 dB by using a 3-mm-long Si7N3 waveguide with a high nonlinear coefficient (∼279 /W/m). Signal quality before and after regeneration is characterized by constellation diagram and error vector magnitude (EVM). The results show that the EVM of the degraded PDM differential phase-shift keying (DPSK) signals with two polarization states of 54% and 53.8%, can be improved to 13.6% and 13.6%, respectively, after regeneration, directly illustrating the remarkable phase noise suppression effect. The applicability of the scheme in PDM quadrature phase shift keying (QPSK) signals was further investigated. Similarly, the EVMs of the two polarization states of the deteriorated QPSK signals are optimized from 28.9% and 29.3% to 13.7% and 13.9%, respectively. The proposed scheme has promising applications in integrated all-optical processing systems and long-distance transmission of optical communications