Average Neural Face Embeddings for Gender Recognition

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Avrupa Bilim ve Teknoloji Dergisi
Ejosat Special Issue:(ARACONF) Special Issue
Average Neural Face Embeddings for Gender Recognition

Average Neural Face Embeddings for Gender Recognition

Authors : Semiha MAKİNİST, Betül AY, Galip AYDIN

Pages : 522-527

Doi:10.31590/ejosat.araconf67

View : 121 | Download : 15

Publication Date : 2020-04-01

Article Type : Research Paper

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

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