\n",
"Nazar Khan\n",
" CVML Lab\n",
" University of The Punjab\n",
"
"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# **Part 1: Face Recognition**\n",
"\n",
"This tutorial shows how to use Principal Component Analysis (PCA) for face recognition. The goal is to introduce PCA concepts while applying them to a real-world problem of recognizing faces from images.\n",
"\n",
"---\n",
"\n",
"## 1. **Introduction to PCA**\n",
"PCA is a dimensionality reduction technique. For face recognition:\n",
"- It helps reduce the large-dimensional pixel space to a smaller-dimensional \"face space.\"\n",
"- It captures the most significant variance (principal components) in the data.\n",
"- Each face image can be represented as a linear combination of these principal components.\n",
"\n",
"---\n",
"\n",
"## 2. **Dataset**\n",
"We'll use the **Labeled Faces in the Wild (LFW)** dataset, available in `sklearn.datasets`.\n",
"\n",
"---\n",
"\n",
"## 3. **Code Implementation**\n",
"\n",
"### 3.1 Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.datasets import fetch_lfw_people\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Load and Visualize the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of samples: 1560, Image shape: 50x37, Number of classes: 12\n"
]
},
{
"data": {
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