Abstract: A hair feature analysis method and system are provided. The hair feature analysis method includes the steps of capturing high magnification and low magnification images of a scalp area and performing preprocessing on these images. The preprocessed high magnification and low magnification images are then input into an artificial intelligence model to simultaneously detect hair follicles and calculate hair widths in the scalp area. Hair characteristics are then calculated based on the analyzed high magnification and low magnification images. The artificial intelligence model can be an R-CNN model or a variant thereof.

Abstract: The present disclosure relates to an automatic scalp sebum classification system and a method for automatic classification of scalp sebum. A high-resolution image of a part of the scalp is captured and divided into non-overlapping blocks. Random rotation and position swapping of the blocks are performed for data augmentation and privacy preservation. Each block is then converted into a one-dimensional vector, to which positional information is added through learnable embedding vectors. The vectors are input into a visual transformation model that outputs a sebum classification label. The automatic scalp sebum classification system and method for automatic classification of scalp sebum offer advantages in speed, accuracy, and privacy preservation over traditional and other AI-based methods.

Abstract: A hair feature analysis method and system are provided. The hair feature analysis method includes the steps of capturing high magnification and low magnification images of a scalp area and performing preprocessing on these images. The preprocessed high magnification and low magnification images are then input into an artificial intelligence model to simultaneously detect hair follicles and calculate hair widths in the scalp area. Hair characteristics are then calculated based on the analyzed high magnification and low magnification images. The artificial intelligence model can be an R-CNN model or a variant thereof.

Abstract: A smart dandruff analysis system and method are provided for analyzing a severity of a subject's dandruff, and the smart dandruff analysis system has operation module, first neural network module, second neural network module, and classification module. The operation module receives a scalp area image of the subject and transforms the scalp area image into a first feature map. The first neural network module, a Convolutional Neural Network model, electrically connects with the operation module for receiving and transforming the scalp area image into a second feature map. The second neural network module, a Transformer model, electrically connecting with the first neural network module for receiving and transforming the second feature map into a third feature map. The classification module electrically connects with the second neural network module for receiving the third feature map and outputting a rating, wherein the rating is to determine the severity of the subject's dandruff.

Abstract: A smart dandruff analysis system and method are provided for analyzing a severity of a subject's dandruff, and the smart dandruff analysis system has operation module, first neural network module, second neural network module, and classification module. The operation module receives a scalp area image of the subject and transforms the scalp area image into a first feature map. The first neural network module, a Convolutional Neural Network model, electrically connects with the operation module for receiving and transforming the scalp area image into a second feature map. The second neural network module, a Transformer model, electrically connecting with the first neural network module for receiving and transforming the second feature map into a third feature map. The classification module electrically connects with the second neural network module for receiving the third feature map and outputting a rating, wherein the rating is to determine the severity of the subject's dandruff.