Biometrics Performance Improvement of Face Recognition Smart Door Using Binary Classifier

Taeseong Kim, Changsoo Eun, Jongwon Park

http://doi.org/10.5626/JOK.2023.50.7.598

Face recognition based smart door is a biometric system that collects images using a camera and decides whether a visitor is registered by recognizing the face. Recently, with increasing number of single-person households, demand for access convenience has increased. Accordingly, research on smart doors using face recognition method is active. Face recognition based smart doors use deep learning method to recognize visitor"s faces. Difference between the visitor"s face and the registrant"s face is converted into a distance through encoding. If the distance between the two faces is less than the threshold value, the door is opened as it is determined to be the same person. Facial similarity thresholds differ according to region, race, and clothing cultures. Also, biometrics performance varies according to threshold settings. In previous studies, a constant of 0.4 was used as the facial similarity threshold, which was the criterion for determining registration. In this paper, facial similarity thresholds were calculated using five binary classifiers and biometric performance was compared. As a result of the experiment using the LFW dataset, the average EER was improved by 16.59% compared to that when the constant was used.