End-to-End Face Detection & Recognition System
A notebook-based computer vision pipeline that separates face localization from identity matching with embeddings.
Problem
How should a face-analysis pipeline separate localization from identity matching so that each stage can be inspected independently?
The project is organized as two notebook experiments: face detection and face recognition. The separation makes the data flow and model assumptions visible. The notebooks are the evidence for this entry; this page does not claim biometric accuracy, production deployment, or suitability for security decisions.
Part 1: Face Detection
The first step in any facial analysis pipeline is accurately locating faces. In this module, we explore techniques to detect faces in static images and video streams. We examine the trade-offs between speed and accuracy using different algorithms.
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Part 2: Face Recognition
Once faces are detected, the next step is identity matching. This module studies numerical embeddings and distance in an embedding space. A threshold and evaluation protocol are still required before distance can be interpreted as a reliable identity decision.
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Key Technical Concepts
1. Face Detection Algorithms
- Haar Cascades: Fast and lightweight, suitable for real-time applications on edge devices, though less robust to occlusions.
- HOG (Histogram of Oriented Gradients): More robust to lighting variations and pose changes than Haar Cascades.
- MTCNN (Multi-task Cascaded Convolutional Networks): Deep learning-based detection that achieves state-of-the-art accuracy but requires more computational resources.
2. Deep Metric Learning
Instead of classifying faces directly, we train a network to output a 128-dimensional embedding where similar faces are grouped together. This Triplet Loss approach ensures that the distance between an anchor image and a positive match is minimized, while the distance to a negative match is maximized.
3. Vector Similarity Search
Identity verification is reduced to a nearest-neighbor search in the embedding space. This allows the system to scale to recognizing thousands of individuals without retraining the core model—only the reference database needs to be updated.
Product value and limits
This proof-of-concept shows the structure of a two-stage computer vision pipeline. The separation is useful because detection errors and identity-matching errors can be inspected and improved independently. Any use in authentication, security, or other high-impact settings would require separate testing for accuracy, false matches, bias, privacy, consent, and operational failure modes.