• 게재일

  2023.06.23

• 저자

  Semin Kim; Chanhyuk Lee; Geunho Jung; Huisu Yoon; Jongha Lee

• URL

  TBD

• 논문요약

  Facial acne is a common skin condition that can easily occur in oily skin. Since acne is a small area and its occurrence largely depends on the skin condition, it is difficult to detect it accurately. In this paper, we propose a new method for detecting f

APA: IEEE CBMS 2023

Abstract

Facial acne is a common skin condition that can easily occur in oily skin. Since acne is a small area and its occurrence largely depends on the skin condition, it is difficult to detect it accurately. In this paper, we propose a new method for detecting facial acne based on semantic segmentation. As the layers in a typical CNN-based deep learning model become deeper, the spatial dimension decreases and the number of channels increases. However, since acne is a small object, its spatial information can be lost as the layers get deeper, and it is critical for detecting facial acne. To alleviate this problem, we propose a center point loss, which maintains the center of the acne even in the reduced spatial dimension of the layers and improves the detection performance. First, we generated a center point ground truth indicating the center of each acne from an acne ground truth and applied two max-pooling layers having different kernel sizes, respectively. Following that, center point maps were obtained from the acne segmentation model's two deep decoders, and center point losses were calculated with the center point ground truth. In addition, a semantic segmentation loss was computed by comparing the final feature map and the acne ground truth. Finally, we used the center point losses and the segmentation loss to train the acne segmentation model. Our proposed method was tested using images obtained from a commercial image acquisition system using facial skin analysis equipment. In our experiments, the performance was improved by using the center point loss, which showed higher IoU performance than the existing deep supervision method that used multi-losses for decoders.