Generating Embedding Features Using Deep Learning for Ethnics Recognition

Abstract

Although significant advances have been made recently in the field of ethnics recognition through face recognition, there is still a lack of studies of ethnics recognition through facial recognition. This study is concerned with ethnics recognition through facial representation using a few images used as samples for any selected group of ethnics using a deep neural network with a Variational Feature Learning (VFL) loss function that has been used to increase the performance accuracy during the evaluation process. The output of a deep neural network is an embedding of 128 bytes for each face image in each group of ethnics. After that, all embeddings of every face in each group of ethnics pass to a machine learning classification method like a Support Vector Machine (SVM). We achieved state-of-the-art ethnic recognition. The system achieved a classification accuracy of 97.3% on a collected group of image dataset collected from three different countries.

A proposed model for enhancing e-bank transactions: an experimental comparative study

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