Face Recognition
Face recognition is one of the most widely used and most discussed applications of Computer Vision.
It allows machines to identify or verify a person based on their facial features. From smartphones to airports, face recognition systems are now part of everyday life.
In this lesson, you will understand how face recognition works, why it is difficult, and how modern systems achieve high accuracy.
What Is Face Recognition?
Face recognition is the task of determining who a person is from an image or a video frame.
It is different from face detection.
- Face detection: Where is a face?
- Face recognition: Whose face is it?
Recognition requires learning unique identity features.
Face Detection vs Face Recognition
A complete face recognition system usually works in two steps:
- Detect faces in the image
- Recognize each detected face
Detection is only the entry point. Recognition is the real challenge.
Why Face Recognition Is Hard
Human faces may look similar, but small variations matter.
- Lighting changes
- Pose variations
- Facial expressions
- Aging
- Occlusion (mask, glasses, beard)
The model must recognize identity despite these changes.
Traditional Face Recognition Approaches
Earlier face recognition systems relied on handcrafted features.
- Eigenfaces (PCA-based)
- Fisherfaces (LDA-based)
- LBPH (Local Binary Patterns Histogram)
These methods worked in controlled environments, but failed in real-world conditions.
Modern Face Recognition (Deep Learning)
Modern systems use deep neural networks to learn facial representations.
Instead of rules, the network learns what makes a face unique.
The output is usually a numeric vector called an embedding.
Face Embeddings
A face embedding is a compact numeric representation of a face.
Faces of the same person produce embeddings that are close together. Different people produce embeddings far apart.
This converts recognition into a distance comparison problem.
Similarity Measurement
Once embeddings are generated, comparison is simple.
- Cosine similarity
- Euclidean distance
If the distance is below a threshold, the faces match.
Verification vs Identification
Face recognition problems fall into two categories:
- Verification (1:1): Is this person who they claim?
- Identification (1:N): Who is this person?
Verification is easier than identification.
Face Recognition Pipeline
Image → Face Detection → Alignment → Feature Extraction → Embedding Comparison → Decision
Each step improves robustness and accuracy.
Face Alignment
Alignment normalizes the face before recognition.
- Eyes aligned horizontally
- Face centered
- Scale normalized
Alignment reduces pose-related errors.
Popular Face Recognition Models
Some widely used deep learning models include:
- FaceNet
- ArcFace
- VGGFace
- DeepFace
These models are trained on millions of face images.
Real-World Applications
- Smartphone unlocking
- Airport security
- Attendance systems
- Surveillance
- Photo organization
Accuracy and ethics both matter here.
Limitations and Challenges
Despite high accuracy, face recognition still faces issues:
- Bias in training data
- Privacy concerns
- Performance across demographics
Responsible deployment is critical.
Ethical Considerations
Face recognition raises serious ethical questions.
- User consent
- Data storage
- Misuse of surveillance
Many regions regulate its use.
Practice Questions
Q1. What is the difference between face detection and recognition?
Q2. What is a face embedding?
Q3. Why is alignment important?
Mini Assignment
Think about a phone face unlock system.
- Which steps must be fast?
- Where could errors occur?
Answer conceptually.
Quick Recap
- Face recognition identifies people
- Uses embeddings and similarity
- Deep learning dominates modern systems
- Alignment improves accuracy
- Ethics and privacy are important
Next lesson: Pose Estimation.