Araştırma Makalesi

Average Neural Face Embeddings for Gender Recognition

1 Nisan 2020
  • Semiha Makinist
  • Betül Ay
  • Galip Aydın
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Average Neural Face Embeddings for Gender Recognition

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

Teşekkür

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).

Kaynakça

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  7. Eidinger, E., Enbar, R., & Hassner, T. (2014). Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security, 9(12), 2170-2179.
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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

1 Nisan 2020

Gönderilme Tarihi

15 Mart 2020

Kabul Tarihi

30 Mart 2020

Yayımlandığı Sayı

Yıl 2020

Kaynak Göster

APA
Makinist, S., Ay, B., & Aydın, G. (2020). Average Neural Face Embeddings for Gender Recognition. Avrupa Bilim ve Teknoloji Dergisi, 522-527. https://doi.org/10.31590/ejosat.araconf67

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