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Abstract
Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate skincare products can exacerbate the condition of the skin. In view of this, this work proposes the use of computer vision (CV) technology to realize a new business model of facial skincare products. The overall framework is composed of a finger vein identification system, skincare products’ recommendation system, and electronic payment system. A finger vein identification system is used as identity verification and personalized service. A skincare products’ recommendation system provides consumers with professional skin analysis through skin type classification and acne detection to recommend skincare products that finally improve skin issues of consumers. An electronic payment system provides a variety of checkout methods, and the system will check out by finger-vein connections according to membership information. Experimental results showed that the equal error rate (EER) comparison of the FV-USM public database on the finger-vein system was the lowest and the response time was the shortest. Additionally, the comparison of the skin type classification accuracy was the highest.
Highlights
Since most people have not received professional medical knowledge training, if they use inappropriate products at will, they will become self-defeating and make the skin condition more serious, which will cost much money and time to remedy, and even lead to repeated skin issues such as acne
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Summary
Since most people have not received professional medical knowledge training, if they use inappropriate products at will, they will become self-defeating and make the skin condition more serious, which will cost much money and time to remedy, and even lead to repeated skin issues such as acne. Based on the concept of unmanned stores, a fast and contactless finger-vein identification system was designed to allow consumers to instantly verify their membership. This identification system can be used as a checkout feature to save time for taking out wallets and credit cards, and thereby improves shopping efficiency by saving time in queuing. Goyal et al [8] identified skin melanoma by a region-based convolutional neural network (R-CNN) [9], and the accuracy rate reached 98%. Adegun et al [10] used a deep convolutional neural network (DCNN) and the subnetwork in the encoder/decoder architecture to identify pigmented tumors on the skin surface, and the accuracy rate reached 96%. We proposed to use multi-feature classification by the ML method and pixel-wise segmentation by the DL method
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