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All Journal JOIV : International Journal on Informatics Visualization
Siew-Chin Chong
Multimedia University, Melaka, Malaysia
Computer Science & IT

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Comparative Analysis of VGG-16 and ResNet-50 for Occluded Ear Recognition Tey, Hua-Chian; Chong, Lee Ying; Chong, Siew-Chin
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2276

Abstract

Occluded ear recognition is a challenging task in biometric systems due to the presence of occlusions that can hinder accurate identification. There is still a research gap in enhancing the robustness of deep learning to handle severities of occlusions with different datasets. This research focuses on developing a robust occluded ear recognition system by implementing fine-tuning techniques on three popular pre-trained deep learning models, Residual Neural Network (ResNet-50), Visual Geometry Group (VGG-16), and EfficientNet. The system is evaluated on two manually occluded ear datasets, which are the AMI ear dataset and the IITD ear dataset. The experiment results showed the fine-tuned ResNet-50 model performs better than the fine-tuned VGG-16 model. The results indicate that the model's ability to accurately predict the classes or labels decreases as more data is occluded. Higher occlusion rates lead to a loss of important information, making it more challenging for the model to distinguish between different patterns and make accurate predictions. According to the findings, the amount of occlusion influenced the identification accuracy and worsened as the occlusion became larger. In the future, ear recognition systems will likely continue to improve in accuracy and be adopted by a wider range of organizations and industries. They may also be integrated with other biometric technologies and used for personalization purposes. However, ethical considerations related to the use of ear recognition systems will also need to be addressed.
2.5D Face Recognition System using EfficientNet with Various Optimizers Teo, Min-Er; Chong, Lee-Ying; Chong, Siew-Chin; Goh, Pey-Yun
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3030

Abstract

Face recognition has emerged as the most common biometric technique for checking a person's authenticity in various applications. The depth characteristic that exists in 2.5D data, also known as depth image, is utilized by the 2.5D facial recognition algorithm to supply additional details, strengthening the system's precision and durability. A deep learning approach-based 2.5D facial recognition system is proposed in this research. The accuracy of 2.5D face recognition could be enhanced by integrating depth data with deep learning approaches. Besides, optimizers in the deep learning approach act as a function for adjusting the properties, like learning rates and weights in the neural network, which can minimize the overall loss of the system and further enhance performance. In this paper, several experiments have been conducted in two versions of EfficientNet architectures, such as EfficientNetB1 and EfficientNetB4, using different optimizers, including Adam, Nadam, Adamax, RMSProp, etc. Various optimizers are compared to find the most suitable optimizer for the system. The Face Recognition Grand Challenge version 2 (FRGC v2.0) database was utilized in this research. This research aims to increase the 2.5D face recognition system’s effectiveness and efficiency by implementing deep learning approaches. Based on the experimental result, a deep learning algorithm enhances the system's accuracy rate. It also proves that the EffifientNetB4, using Adam optimizer, gained the highest accuracy rate at 97.93%.
Co-Authors Chong, Lee Ying Chong, Lee-Ying Goh, Pey-Yun Teo, Min-Er Tey, Hua-Chian