This study introduces a novel approach to investigate human facial attractiveness’s intrinsic psychophysical function using a sequential experimental design with a combination of Bayesian optimization (BO) and StyleGAN2. To estimate a facial attractiveness function from pairwise comparison data, we used a BO that incorporates Gaussian process preference learning (GPPL). Fifty female Japanese university students provided facial photographs. We embedded each female facial image into a latent representation (18 × 512 dimensions) in the StyleGAN2 network trained on the Flickr-Faces-HQ (FFHQ) dataset. Using PCA, the latent representations’ dimension is reduced to an 8-dimensional subspace, which we refer to here as the Japanese female face space. Nine participants participated in the pairwise comparison task. They had to choose the more attractive facial images synthesized using StyleGAN2 in the face subspace and provided their evaluations in 100 trials. The stimuli for the first 80 trials were created from randomly generated parameters in the face subspace, while the remaining 20 trials were created from the parameters calculated using the acquisition function. We estimated the facial parameters corresponding to the most, the least, 25, 50, 75 percentile rank of attractiveness and reconstructed the faces based on the results. The results show that a combination of StyleGAN2 and GPPL methodologies is an effective way to elucidate human kansei evaluations of complex stimuli such as human faces.