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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. [ABSTRACT FROM AUTHOR]

Today, biometrics is being widely used in various fields. Finger-vein is a type of biometric information and is based on finger-vein patterns unique to each individual. Various spoofing attacks have recently become a threat to biometric systems. A spoofing attack is defined as an unauthorized user attempting to deceive a system by presenting fake samples of registered biometric information. Generally, finger-vein recognition, using blood vessel characteristics inside the skin, is known to be more difficult when producing counterfeit samples than other biometrics, but several spoofing attacks have still been reported. To prevent spoofing attacks, conventional finger-vein recognition systems mainly use the difference in texture information between real and fake images, but such information may appear different depending on the camera. Therefore, we propose a method that can detect forged finger-vein independently of a camera by using remote photoplethysmography. Our main idea is to get the vital sign of arterial blood flow, a biometric measure indicating life. In this paper, we selected the frequency spectrum of time domain signal obtained from a video, as the feature, and then classified data as real or fake using the support vector machine classifier. Consequently, the accuracy of the experimental result was about 96.46%. [ABSTRACT FROM AUTHOR]