<![CDATA[This research carries the theme of facial recognition to detect visitors' faces by counting the number of times visitors make transactions. The objective of this research is to develop and implement a face verification system for public purposes, such as commercial purposes. One potential application of this system is in the realm of promotions, where it could be utilized to track the number of transactions conducted by visitors. The method employed utilizes deep metric learning (DML) to generate a model capable of verifying various facial images through the Convolutional Neural Network (CNN) architecture, which is designed to train human face image data. The triplet loss method is employed in training data due to its recognition as a more flexible approach in utilizing labels (in the form of face images) to facilitate comparison with the detected face images. The model employed for face recognition applications is facenet, a system that has been demonstrated to achieve a high degree of accuracy. The research's output is an application capable of swiftly and precisely verifying facial images of visitors and calculating the number of visitor transactions. The number of visitor transactions can subsequently be utilized as a promotional or discount strategy in commercial services.]]>
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