Neural Network Facial Authentication for Public Electric Vehicle Charging Station
This addresses facial recognition accuracy issues for Asian users in authentication systems, though it is incremental as it compares existing methods on a specific dataset.
This study compared Dlib ResNet and KNN classifiers for facial recognition accuracy on an Asian ethnicity dataset, finding that Dlib ResNet had a deficiency in this context. The research demonstrated a practical application by implementing authentication at electric vehicle charging stations.
This study is to investigate and compare the facial recognition accuracy performance of Dlib ResNet against a K-Nearest Neighbour (KNN) classifier. Particularly when used against a dataset from an Asian ethnicity as Dlib ResNet was reported to have an accuracy deficiency when it comes to Asian faces. The comparisons are both implemented on the facial vectors extracted using the Histogram of Oriented Gradients (HOG) method and use the same dataset for a fair comparison. Authentication of a user by facial recognition in an electric vehicle (EV) charging station demonstrates a practical use case for such an authentication system.