AI Lesson 88 – Face Detection & Recognition | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 88: Face Recognition

Face recognition is a computer vision technique that identifies or verifies a person using their facial features. Unlike face detection, which only finds faces in an image, face recognition answers the question of who the person is.

This technology has become common because human faces contain unique patterns that can be learned and compared by machines.

Real-World Connection

Face recognition is used to unlock smartphones, verify identity at airports, tag people in photos, monitor attendance, and enhance security systems.

Any application that needs identity verification without physical contact relies on face recognition.

What Is Face Recognition?

Face recognition is the process of comparing a detected face with known faces stored in a database and finding the closest match.

  • Detects a face first
  • Extracts facial features
  • Matches features against stored data

How Face Recognition Works

Most face recognition systems follow this pipeline:

  • Detect the face region in the image
  • Align and normalize the face
  • Extract feature vectors
  • Compare features with known faces

Modern systems use deep learning models to learn facial embeddings automatically.

Face Detection vs Face Recognition

Understanding the difference is important:

  • Face Detection: Finds where faces are
  • Face Recognition: Identifies whose face it is

Simple Face Recognition Example

Below is a basic example using OpenCV’s LBPH (Local Binary Patterns Histogram) face recognizer. 


import cv2

face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("trained_model.yml")

image = cv2.imread("test.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

for (x, y, w, h) in faces:
    face = gray[y:y+h, x:x+w]
    label, confidence = recognizer.predict(face)
    cv2.rectangle(image, (x,y), (x+w,y+h), (0,255,0), 2)
    cv2.putText(image, f"ID: {label}", (x, y-10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2)

cv2.imshow("Face Recognition", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

What This Code Is Doing

The program detects faces in the image, extracts the face region, and compares it with trained facial data.

The recognizer returns an ID and a confidence score indicating how closely the face matches known identities.

Understanding the Output

The output image shows bounding boxes around faces along with predicted IDs. Lower confidence values usually indicate better matches.

In real applications, IDs are mapped to names or user profiles.

Popular Face Recognition Approaches

  • LBPH: Lightweight and simple
  • Eigenfaces: PCA-based method
  • Deep Face Embeddings: High accuracy using deep learning

Ethical and Privacy Considerations

Face recognition raises privacy and ethical concerns. Responsible systems must ensure transparency, consent, and secure data handling.

Many countries regulate the use of facial recognition technology.

Practice Questions

Practice 1: Face recognition helps determine what about a person?



Practice 2: Which step happens before face recognition?



Practice 3: What is extracted from a face for comparison?



Quick Quiz

Quiz 1: Which task identifies a person by face?





Quiz 2: Which algorithm is used in the example?





Quiz 3: What major concern surrounds face recognition?





Coming up next: Feature Detection — identifying key points and patterns in images.

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