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INDONESIA
International Journal of Intelligent Systems and Applications in Engineering
Published by Ismail SARITAS
ISSN : 21476799     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.
Arjuna Subject : -
Articles 11 Documents
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Search results for , issue " Vol 6, No 3 (2018)" : 11 Documents clear
Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks Hacibeyoglu, Mehmet; Ibrahim, Mohammed Hussein
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018644778

Abstract

The interest in automatic gender classification has increased rapidly, especially with the growth of online social networking platforms, social media applications, and commercial applications. Most of the images shared on these platforms are taken by mobile phone with different expressions, different angles and low resolution. In recent years, convolutional neural networks have become the most powerful method for image classification. Many researchers have shown that convolutional neural networks can achieve better performance by modifying different network layers of network architecture. Moreover, the selection of the appropriate activation function of neurons, optimizer and the loss function directly affects the performance of the convolutional neural networks. In this study, we propose a gender classification system from facial images taken by mobile phone using convolutional neural networks. The proposed convolutional neural networks have a simple network architecture with appropriate parameters can be used when rapid training is needed with the amount of limited training data. In the experimental study, the Adience benchmark dataset was used with 17492 different images with different gender and ages. The classification process was carried out by 10-fold cross validation. According the experimental results, the proposed convolutional neural networks predicted the gender of the images 98.8% correctly for training and 89.1% for testing.

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