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Home AI News Face Recognition: Siamese Networks & Binary Classification

Face Recognition: Siamese Networks & Binary Classification

Updated on Nov 06,2025

Table of Contents

Face recognition is a critical technology in a variety of applications, from security systems to social media. While triplet loss is a well-established method for training convolutional neural networks (CNNs) for face recognition, another approach involves framing the problem as a binary classification task. This approach uses Siamese networks and offers a unique way to learn the parameters needed for accurate face verification. The following discussion explores how this can be achieved, using a pair of neural networks to create embeddings.

Key Points

Siamese networks can be used for face verification.

Face recognition can be approached as a binary classification problem.

Learning similarity functions involves logistic regression.

Pre-computing embeddings can significantly improve deployment efficiency.

Data can be collected, training, evaluate and deploy the model.

Face verification and recognition could be trained in a binary classification problem, instead of the triplet loss.

Understanding Face Recognition and Verification

Face Recognition as Binary Classification

Face recognition is more than just identifying a person; it's about confirming that the person is who they claim to be. This is face verification. One way to approach face verification involves treating it as a binary classification problem. Instead of identifying numerous faces, you train a system to answer a simple question: "Are these two faces the same person?" This binary outcome makes the problem more manageable and computationally efficient. This strategy involves a Siamese network, which consists of two identical neural networks that share the same weights and architecture. Each network processes one of the input images, and their outputs are then compared to produce a similarity score. If the score exceeds a threshold, the faces are considered a match; otherwise, they're deemed different. The goal is to ensure the target output is 1 if both images show the same person and 0 if they show different people. This contrasts with more complex approaches that require differentiating among a large number of known individuals.

The Siamese Network Architecture

The core of this method lies in the Siamese network architecture.

This involves using a pair of identical neural networks, each processing one of the two input images. These networks are designed to compute embeddings, which are high-dimensional vectors that represent the unique characteristics of each face. By comparing these embeddings, the system can determine how similar the two faces are. The embedding process itself typically involves several layers of convolution, pooling, and fully connected layers. Each layer extracts increasingly complex features from the image, ultimately generating a vector that encapsulates the most important aspects of the face. These embeddings might be, for example, 128-dimensional vectors, though higher dimensions are also possible to capture more subtle variations. The key is that both networks in the Siamese architecture share the same parameters. This ensures that the embeddings produced are directly comparable, because they’re generated using the same feature extraction process.

Learning Similarity Functions with Logistic Regression

Using Logistic Regression

To make a final prediction about whether two faces belong to the same person, the embeddings generated by the Siamese network need to be compared. The logistic regression unit takes the embeddings from both networks and applies a sigmoid function to produce a probability score. This score indicates the likelihood that the two faces are of the same person. The input to the logistic regression unit isn't the raw embeddings; instead, it's a set of features derived from those embeddings. One common approach is to calculate the element-wise absolute difference between the two embeddings. This highlights the features that differ the most between the two faces. Other methods, such as the chi-square similarity, are also used. The aim is to create a set of features that are highly discriminative, allowing the logistic regression unit to make accurate predictions. The element-wise differences are then fed into the logistic regression unit, which learns to weight these differences appropriately. If the differences are small, the logistic regression unit outputs a high probability, indicating that the faces are likely the same person. Conversely, if the differences are large, it outputs a low probability, suggesting that the faces are different.

Training the Siamese Network and Logistic Regression

Step-by-Step Training Process

  1. Gathering Training Data: The initial step involves assembling a dataset of face images, labeled to indicate whether pairs of images show the same person or different people. This dataset will be used to train the Siamese network and logistic regression unit.
  2. Setting Up the Siamese Network: Configure two identical CNNs, each with the same architecture and shared weights. These CNNs will learn to generate embeddings from the input face images.
  3. Calculating Feature Differences: Compute element-wise absolute differences between the embeddings produced by the two CNNs for each image pair. These differences will serve as the input features for the logistic regression unit.
  4. Integrating Logistic Regression: Use a logistic regression model to map the feature differences to a probability score, representing the likelihood that the two faces belong to the same person.
  5. Fine-Tuning: Fine-tune this logistic regression layer, adjusting the weighting on those 128 new features (if using 128 dimensions for embedding).
  6. Backpropagation Training: Train the entire system, including the CNNs and logistic regression unit, using backpropagation. The goal is to minimize a loss function that penalizes incorrect predictions. The backpropagation algorithm optimizes the weights and biases of the neural network layers, progressively improving the model's accuracy.
  7. Adjusting Weights: The final logistic regression can have additional parameters such as a weight (W) and Bias (B).
  8. Pre-computing Embeddings: To improve deployment you could pre-compute the embeddings for quick comparison.

Advantages and Disadvantages of Siamese Networks for Face Recognition

👍 Pros

Computationally Efficient

Directly Addresses Face Verification

Effective Feature Extraction

👎 Cons

Training Data Requirements

Potential for Overfitting

Limited Generalization

Frequently Asked Questions

What are Siamese networks, and how do they function in face recognition?
Siamese networks are neural networks containing two or more identical subnetworks. Each subnetwork receives a different input but shares the same weights. In face recognition, these networks process pairs of face images to compute embeddings, which are then compared for similarity.
Why is face recognition sometimes framed as a binary classification problem?
Framing face recognition as a binary classification problem simplifies the task to answering whether two faces are the same, making the system more efficient than differentiating among a large number of individuals. This approach uses Siamese networks to compare face image pairs.
What is the role of logistic regression in learning similarity functions for face recognition?
Logistic regression is used to map the differences between embeddings produced by the Siamese networks to a probability score. This score represents the likelihood that the two faces belong to the same person, enabling the system to make a binary decision.

Related Questions

How does this Siamese network approach compare to traditional methods like triplet loss?
Traditional approaches may involve methods like triplet loss, which aims to learn an embedding space where faces of the same person are closer together and faces of different people are further apart. Siamese networks, framed as a binary classification problem, focus directly on verifying whether two faces are the same, making the process computationally efficient. Each has its advantages based on the specific application and dataset.
Are there other methods to evaluate the similarity of the embeddings?
Yes, there are alternative methods, such as cosine similarity, Euclidean distance, and chi-square similarity. The chi-square similarity formula is another way to treat face recognition. Each method has its own advantages and is suitable for different types of data and applications. For example, cosine similarity is effective for high-dimensional data, while Euclidean distance works well for lower-dimensional data.
What is involved in actually deploying the trained system?
Deployment involves pre-computing embeddings and ensuring that raw images do not have to be stored. The overall system is built using a Siamese network architecture and is to compare to the embeddings.
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