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.
Highlights
The name of deep neural networks has been frequently heard both in image processing and natural language processing
In the early 2000s, serious studies were made in the field of deep learning and this period was accepted as a turning point for the field of artificial intelligence
In 2006, Deep Belief Nets showed how multilayer neural networks will work and how undefined features are learned by the system. These new generation ANNs are named as Deep Net and the studies in this field are gathered under the title of Deep Learning
Summary
The name of deep neural networks has been frequently heard both in image processing and natural language processing. Cha et al [3] adopt a multi-task Deep Convolutional Neural Network (DCNN) method and performed face detection using facial landmarks for different face poses They used the FDDB dataset [4] and as a result of the study it was observed that the method they proposed improved the other state-of-the-art methods by 3%. At the same time, the proposed method requires a complex cascade architecture of deep network Based on this disadvantage, a new tasks-constrained deep convolutional network (TCDCN) reduces model complexity has been presented for facial point detection [6]. Rothe et al [11] presented a model that can predict age and gender on a single image using the deep learning method They used IMDB-WIKI dataset within the scope of the study. Xu et al [19] have been proposing Hierarchical Multi-task Network (HMTNet), a deep neural network that can identify both sex, race, and facial beauty from a person's portrait image
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
