Average Neural Face Embeddings for Gender Recognition

Year 2020, Ejosat Special Issue 2020 (ARACONF), 522 - 527, 01.04.2020

Semiha Makinist Betül Ay Galip Aydın

https://doi.org/10.31590/ejosat.araconf67

Cited By: 1

Abstract

In recent years, with the rise of artificial intelligence and deep learning, facial recognition technologies have been developed that operate with high accuracy even in adverse conditions. However, extracting demographic information such as gender, age and race from facial features has been a hot research area. In this study, a new Average Neural Face Embeddings (ANFE) method that uses facial vectors of people for gender recognition is presented. Instead of training deep neural network from scratch, a simple, fast and effective solution has been developed that performs a distance calculation between the average gender vectors and the person's face vector. The method proposed as a result of the study carried out provided a high and successful recognition performance with with 96.47% of the males and 99.92% of the females.

Keywords

Face Embeddings, Face Detection, Average Embeddings, Gender Recognition, Deep Learning

Thanks

This study was carried under the project “Deep Learning and Big Data Analysis Platform (DEGIRMEN)” supported by Presidency of the Republic of Turkey, Presidency for Defence Industries (SSB).

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