Add/face detection model

docs/projects/computer-vision/face-detection.md

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+
+# Face Detection using OpenCV
+
+
+### AIM 
+The goal of this project is to build a face detection system using OpenCV, which identifies faces in static images using Haar Cascades.
+
+
+### DATASET LINK 
+For this project we are going to use the pretrained Haar Cascade XML file for face detection from OpenCV's Github repository. 
+[Click here to view the dataset](https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml)
+
+
+### NOTEBOOK LINK 
+[Click here to view the notebook](https://colab.research.google.com/drive/1upcl9sa5cL5fUuVLBG5IVuU0xPYs3Nwf#scrollTo=94ggAdg5AnUk)
+
+
+### LIBRARIES NEEDED
+<!-- Mention it in bullet points either in numbering or simple dots -->
+<!-- Mention all the libraries required for the project. You can add more or remove as necessary. -->
+
+??? quote "LIBRARIES USED"
+
+    - OpenCV
+    - Numpy
+    - Matplotlib
+
+--- 
+
+### DESCRIPTION 
+This project involves building a face detection model using OpenCV's pre-trained Haar Cascade Classifiers to detect faces in images.
+
+!!! info "What is the requirement of the project?"
+    - A face detection system is needed for various applications such as security, attendance tracking, and facial recognition systems.
+    - This project demonstrates a basic use of computer vision techniques for detecting faces in static images.
+
+
+??? info "Why is it necessary?"
+    - Face detection is the first crucial step in many computer vision applications such as face recognition and emotion analysis.
+    -  It is an essential component in systems that require human identification or verification.
+
+??? info "How is it beneficial and used?"
+    - Face detection can be used in automation systems, for example, in attendance tracking, photo tagging, and security surveillance.
+    - It enables various applications in user experience enhancement and biometric systems.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+    - I began by exploring OpenCV documentation, focusing on how to implement Haar Cascade for face detection.
+    - Initially, I focused on static image detection, planning to extend the project to video-based detection in the future.
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+    - OpenCV documentation
+    - Book: "Learning OpenCV 3" by Adrian Kaehler and Gary Bradski
+
+--- 
+
+### EXPLANATION 
+
+#### DETAILS OF THE DIFFERENT FEATURES 
+
+    - Haar Cascade Classifier: A machine learning-based approach for detecting objects in images or video. It works by training on a large set of positive and negative images of faces.
+    - Cascade Classifier*: The classifier works through a series of stages, each aimed at increasing detection accuracy.
+    - Face Detection: The primary feature of this project is detecting human faces in static images, which is the first step in many facial recognition systems.
+
+
+
+--- 
+
+#### PROJECT WORKFLOW 
+<!-- Clearly define the step-by-step workflow followed in the project. You can add or remove points as necessary. -->
+
+=== "Step 1"
+
+    Initial data exploration and understanding:
+  - Research the Haar Cascade method for face detection in OpenCV.
+  - Collect sample images for testing the model's performance.
+
+
+=== "Step 2"
+
+    Data cleaning and preprocessing:
+  - Ensure all input images are properly formatted (e.g., grayscale images for face detection).
+  - Resize or crop images to ensure optimal processing speed.
+
+
+=== "Step 3"
+
+    Feature engineering and selection:
+    - Use pre-trained Haar Cascade classifiers for detecting faces.
+    - Select the appropriate classifier based on face orientation and conditions (e.g., frontal face, profile).
+
+
+=== "Step 4"
+
+    Model training and evaluation:
+    - Use OpenCV's pre-trained Haar Cascade models.
+    - Test the detection accuracy on various sample images.
+
+
+=== "Step 5"
+
+    Model optimization and fine-tuning:
+  - Adjust parameters such as scale factor and minNeighbors to enhance accuracy.
+  - Experiment with different input image sizes to balance speed and accuracy.
+
+
+=== "Step 6"
+
+    Validation and testing:
+  - Validate the model's effectiveness on different test images, ensuring robust detection.
+  - Evaluate the face detection accuracy based on diverse lighting and image conditions.
+
+
+--- 
+
+#### PROJECT TRADE-OFFS AND SOLUTIONS 
+<!-- Explain the trade-offs encountered during the project and the solutions you implemented. -->
+
+=== "Trade Off 1"
+    - Accuracy vs. computational efficiency.
+        - Solution: Fine-tuned classifier parameters to ensure a balance between accuracy and speed.
+
+=== "Trade Off 2"
+    - Detection performance vs. image resolution. 
+        - Solution: Optimized input image resolution and processing flow to ensure both fast processing and accurate detection.
+
+--- 
+
+### SCREENSHOTS 
+<!-- Include screenshots, graphs, and visualizations to illustrate your findings and workflow. -->
+
+!!! success "Project workflow"
+
+    ``` mermaid
+      graph LR
+    A[Start] --> B{Face Detected?}
+    B -->|Yes| C[Mark Face]
+    C --> D[Display Result]
+    B -->|No| F[Idle/Do Nothing]
+    ```
+
+--- 
+
+### CONCLUSION 
+
+#### KEY LEARNINGS 
+
+!!! tip "Insights gained from the data"
+     - Gained an understanding of face detection using Haar Cascades. 
+     - Improved ability to optimize computer vision models for accuracy and speed.
+
+??? tip "Improvements in understanding machine learning concepts"
+     - Learned how to handle trade-offs between accuracy and speed in real-time applications. 
+     - Gained hands-on experience with the implementation of object detection algorithms.
+
+??? tip "Challenges faced and how they were overcome"
+    - Challenge: Low detection accuracy in poor lighting conditions. 
+    - Solution: Adjusted classifier parameters and added preprocessing steps to improve accuracy.
+
+--- 
+
+#### USE CASES
+<!-- Mention at least two real-world applications of this project. -->
+
+=== "Application 1"
+
+    **Security Surveillance Systems**
+
+    - Used for identifying individuals or monitoring for intruders in secure areas.
+
+=== "Application 2"
+
+    **Attendance Systems**
+
+    - Used to automate attendance tracking by detecting the faces of students or employees.

docs/projects/computer-vision/index.md

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 # Computer Vision 🎥 
 
+<div style="display: flex; flex-direction: column; gap: 10px;">
+
+<!-- OpenCV Face Detection -->
+<a href="face-detection" style="padding: 0 2px 0 16px; background-color: rgba(39, 39, 43, 0.4); border: 1px solid rgba(76, 76, 82, 0.4); border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1); overflow: hidden; transition: transform 0.2s; display: flex; align-items: center;">
+  <img src="https://imerit.net/wp-content/uploads/2021/07/Face-Detection-in-Images-with-Bounding-Boxes.jpg" alt="OpenCV Logo" style="width: 300px; height: 150px; object-fit: cover; border-radius: 10px;" />
+  <div style="padding: 15px;">
+    <h2 style="margin: 0; font-size: 20px;">Face Detection Model</h2>
+    <p style="font-size: 16px;">Detecting faces in images using OpenCV's powerful Haar cascades.</p>
+    <p style="font-size: 12px;">📅 2025-01-16 | ⏱️ 10 mins</p>
+  </div>
+</a>
+
+</div>