Gender Classification Based on Face Recognition using Convolutional Neural Networks (CNNs)

R. P. Yuda, C. Aroef, Z. Rustam, H. Alatas

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Biometrics are physical or behavior characteristics of human that can be used to identify someone. One form of physical characteristics possessed by humans is fingerprints, retinal scanning, face and hand geometry, while one form of behavioral characteristics possessed by humans is handwriting, signatures, mouse usage analysis, walking patterns, etc. Basically, physical characteristics are more easily observed than behavioral characteristics. Therefore, physical characteristics are more often used in many aspects of security. One of the most common physical characteristics is face. By seeing the face, we can find out or predict how old they are, their gender and even their expression. However, there are still many mistakes in predicting a gender through person's face. In fact, there are still many crimes in falsifying self-identity (such as gender). So, we need a method that is able to classify identity (gender) based on a person's face appropriately. One method that can be used is Convolutional Neural Networks (CNNs). Later, CNNs will classify a person's gender (male / female) based on a person's face image data. And based on.

Original languageEnglish
Article number012042
JournalJournal of Physics: Conference Series
Volume1490
Issue number1
DOIs
Publication statusPublished - 9 Jun 2020
Event5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019 - Surabaya, Indonesia
Duration: 19 Oct 2019 → …

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