Adult Gender Classification Based on Facial Images Using Convolutional Neural Networks
DOI:
https://doi.org/10.54082/jiki.359Keywords:
Convolutional Neural Network, Gender Classification, Face Recognition, UTKFace, Deep LearningAbstract
Gender classification in facial images is a critical component in biometric verification systems and digital service personalization. However, unconstrained environmental conditions such as lighting variations, pose, and occlusion remain major challenges in implementing reliable systems. This study aims to develop and evaluate a lightweight Convolutional Neural Network (CNN) model for gender classification specifically on individuals aged 17 and above using the filtered UTKFace dataset. The methodology includes preprocessing 19,633 images divided into training (80%) and validation data (20%), with a three-layer CNN architecture (filters 32-64-128), ReLU activation functions, MaxPooling, and 0.5 Dropout. Training applied real-time data augmentation and early stopping mechanisms to prevent overfitting. Evaluation results show a global accuracy of 93%, with female precision reaching 96% and male recall at 96%, indicating high reliability in detecting both gender classes in a balanced manner. Error analysis identified three dominant factors causing misclassification: extreme lighting (35%), occlusion (28%), and non-frontal pose (22%). These findings confirm that a lightweight CNN architecture with three convolutional layers can achieve competitive performance for gender classification in unconstrained environments with relatively low computational requirements (1.47 million parameters), making it feasible for implementation as the core unit of automated biometric verification systems on resource-constrained devices.
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Copyright (c) 2026 Salma Dewi, Rihartanto Rihartanto, Hari Purwadi
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