Facerecognition

face-recognition/Readme.md

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+# Face Recognition Project
+
+## Overview
+This repository is part of the ML-CaPsule project, specifically focusing on face recognition using machine learning techniques.
+
+## Features
+- **Face Detection**: Identifies faces in images and video streams.
+- **Face Recognition**: Matches detected faces with known faces.
+- **Real-time Processing**: Capable of processing video feeds for real-time face recognition.
+- **Facial-Attribute Analysis** : Labels the face with {race, gender, age and emotion} 
+
+## Requirements
+- Python >3.9
+- deepface
+- #there are some explicit dependencies
+- requests>=2.27.1
+- numpy>=1.14.0
+- pandas>=0.23.4
+- gdown>=3.10.1
+- tqdm>=4.30.0
+- Pillow>=5.2.0
+- opencv-python>=4.5.5.64
+- tensorflow>=1.9.0
+- keras>=2.2.0
+- Flask>=1.1.2
+- mtcnn>=0.1.0
+- retina-face>=0.0.1
+- fire>=0.4.0
+- gunicorn>=20.1.0
+
+## Installation
+1. Clone the repository:
+    ```bash
+    git clone https://github.com/Raghucharan16/ML-CaPsule.git
+    cd ML-CaPsule/face-recognition
+    ```
+2. Install the required packages:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Usage
+1. run the deepface.ipynb cells for required operation.
+
+
+## Contributing
+Contributions are welcome! Please open an issue or submit a pull request.

face-recognition/deepface.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68d0e7bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deepface import DeepFace\n",
+    "detector_backends = [ 'opencv', 'retinaface',\n",
+    "        'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface'] # or 'skip' (default is opencv)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74c9e27e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Faces Detection\n",
+    "faces=DeepFace.extract_faces(img_path=\"img.jpg\", #select the image [it can have multiple faces]\n",
+    "                             detector_backend = 'mtcnn', #[select the backend among mtcnn, opencv, ssd, dlib]\n",
+    "                             enforce_detection = False, #pass this argument to disable multiple face detection\n",
+    "                             align = True, #pass this argument to align faces\n",
+    "                             margin = 10) #add margin for face extraction\n",
+    "print(faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c46a578",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Facial Recognition\n",
+    "models = [\n",
+    "  \"VGG-Face\", \n",
+    "  \"Facenet\", \n",
+    "  \"Facenet512\", \n",
+    "  \"OpenFace\", \n",
+    "  \"DeepFace\", \n",
+    "  \"DeepID\", \n",
+    "  \"ArcFace\", \n",
+    "  \"Dlib\", \n",
+    "  \"SFace\",\n",
+    "  \"GhostFaceNet\",\n",
+    "]\n",
+    "#You can adjust the threshold according to your use case. Print the result and see the distance values. Then, you can decide the optimal threshold for your project.\n",
+    "#you can use any of the these models for verify and find methods for recognition\n",
+    "fr_result = DeepFace.verify(\n",
+    "  img1_path = \"img1.jpg\",\n",
+    "  img2_path = \"img2.jpg\",\n",
+    ")\n",
+    "print(fr_result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0cd963f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Deepface's find method\n",
+    "dfs = DeepFace.find(\n",
+    "  img_path = \"img1.jpg\",\n",
+    "  db_path = \"PATH_TO_YOUR_DB\"\n",
+    ")\n",
+    "print(dfs) #you can print the result to see the distance values\n",
+    "\n",
+    "#Facial Analysis\n",
+    "objs = DeepFace.analyze(\n",
+    "  img_path = \"img1.jpg\", \n",
+    "  actions = ['age', 'gender', 'race', 'emotion'],\n",
+    ")\n",
+    "print(objs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ab050cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Facial Embeddings\n",
+    "embedding_objs = DeepFace.represent(\n",
+    "  img_path = \"img1.jpg\",\n",
+    "  model_name = models[2],\n",
+    ")\n",
+    "#These can be used for clustering, finding similarity between faces, vector operations, by storing in a vector database for faster retrieval, etc."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

face-recognition/requirements.txt

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+deepface