- Research Article
- 10.59088/gi.v3i4.21
Facial Verification Using a Siamese Neural Network: A Deep Learning Approach for Identity Authentication
- Aug 12, 2025
- Global Social Science and Humanities Journal
- Abdulrahman Alsabri + 2 more +2
Facial recognition has become an important technology in the domain of biometric authentication, thereby providing greater protection to access control and identity verification applications. This work presents a facial verification system that is new and based on a Siamese neural network. Its operation is based on having deep convolutional architectures that extract strong feature embeddings for facial images. The proposed system employs the Labeled Faces in the Wild (LFW) dataset for training negative samples and was further enhanced using custom-captured positive and anchor images taken from the webcam with extensive data augmentation to mimic real-life variations in illumination, pose, and occlusion. The architecture of the network consists of an embedding layer to extract 4096-dimensional feature vectors, an L1 distance layer that measures the similarity between pairs of images, and a classification layer to predict the verification outcomes using a binary cross-entropy loss function optimized through the Adam algorithm. Experimental results indicate high precision and recall, showing near-unity performance on benchmark tests and real-time verification scenarios. Those results further highlight the capability of Siamese networks for one-shot learning in biometric applications, minimizing their dependence on large labeled datasets. Future work will focus on the scalability to larger and more heterogeneous datasets and integration with multimodal biometric systems. Generally, this research presents a promising framework for secure, efficient, and robust facial verification for real-world applications.
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