Exploring the Facial Color Representative Regions Using the Humanae Images

Abstract

It is difficult to describe facial skin color through a solid color as it varies from region to region. In this article, the authors utilized image analysis to identify the facial color representative region. A total of 1052 female images from Humanae project were selected as a solid color was generated for each image as their representative skin colors by the photographer. Using the open CV-based libraries, such as EOS of Surrey Face Models and DeepFace, 3448 facial landmarks together with gender and race information were detected. For an illustrative and intuitive analysis, they then re-defined 27 visually important sub-regions to cluster the landmarks. The 27 sub-region colors for each image were finally derived and recorded in L∗, a∗, and b∗. By estimating the color difference among representative color and 27 sub-regions, we discovered that sub-regions of below lips (low Labial) and central cheeks (upper Buccal) were the most representative regions across four major ethnicity groups. In future study, the methodology is expected to be applied for more image sources.

Unrestricted Face Recognition Algorithm Based on Improved Residual Network IR-ResNet-SE

“…And in the case of difficulties in further upgrading and optimization of network structure, researchers gradually turned their attention to the field of loss function and attention network [8]. In 2014, the proposal of DeepFace [9] network made significant progress in the direction of face recognition. Because It was the first time that deep learning was applied to face recognition, the accuracy was 97.35% in the public dataset LFW.…”