Facial Recognition with Siamese Network
Computer vision and deep learning
One-Shot Face Recognition
A CNN-based Siamese Network for one-shot facial recognition, trained with contrastive learning and optimized data pipelines using TensorFlow, OpenCV, NumPy, and Matplotlib.
Problem
Face recognition often has limited examples per person
Traditional classification models need many labeled examples per identity. A Siamese Network learns similarity instead, making it useful for verification tasks where only one or a few reference images are available.
Pairwise learning
The model learns whether two face images belong to the same identity rather than predicting a fixed class label.
Contrastive signal
Positive and negative image pairs help the network learn a distance space where similar faces stay close.
Practical use cases
The approach fits access control, personal device unlocking, camera systems, and lightweight identity verification flows.
Model
Architecture
The model uses an input size of 105 x 105 x 3, convolution and pooling blocks, an L1 distance layer, and a fully connected prediction head with sigmoid output.
