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Face recognition is often seen as a complex task, and many people are intimidated by the term. When you hear the word "face recognition," it might seem like something only experts can handle. But in reality, if you don't dive deep into the theory, implementing face recognition isn't that hard. Today, we'll show you how to build a simple face recognition system using just 40 lines of code.
It's important to understand the difference between face detection and face recognition. Face detection identifies whether there’s a face in an image, while face recognition determines who that face belongs to. Many online tutorials confuse the two, but they're fundamentally different tasks. For this project, we're focusing on face recognition.
To get started, we'll use Python with several key libraries: Dlib, Scikit-image, and Anaconda. Dlib is a powerful C++-based library that includes tools for machine learning, image processing, and more. It also provides a Python interface, making it easy to use. You can install it via pip with `pip install dlib`, and Scikit-image can be installed with `pip install scikit-image`.
The main files we’ll need are:
- `shape_predictor_68_face_landmarks.dat`: A trained model for detecting facial landmarks.
- `dlib_face_recognition_resnet_model_v1.dat`: A pre-trained ResNet model for face recognition.
- Candidate images: These are the faces we want the system to recognize.
- A test image: This is the image we want to identify.
Once everything is set up, the process involves three main steps:
1. Extract facial descriptors from each candidate image.
2. Detect and extract the facial descriptor from the test image.
3. Compare the test descriptor with the candidate descriptors using Euclidean distance. The closest match is the identified person.
Here’s a simplified version of the code used:
```python
# Import necessary libraries
import sys, os, dlib, glob, numpy
from skimage import io
# Check command line arguments
if len(sys.argv) != 5:
print("Please check if the parameters are correct")
exit()
# Load models and paths
predictor_path = sys.argv[1]
face_rec_model_path = sys.argv[2]
faces_folder_path = sys.argv[3]
img_path = sys.argv[4]
# Initialize detectors and models
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)
# Process candidate faces
descriptors = []
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
img = io.imread(f)
dets = detector(img, 1)
for k, d in enumerate(dets):
shape = sp(img, d)
face_descriptor = facerec.compute_face_descriptor(img, shape)
descriptors.append(numpy.array(face_descriptor))
# Process test image
img = io.imread(img_path)
dets = detector(img, 1)
dist = []
for k, d in enumerate(dets):
shape = sp(img, d)
face_descriptor = facerec.compute_face_descriptor(img, shape)
d_test = numpy.array(face_descriptor)
for i in descriptors:
dist.append(numpy.linalg.norm(i - d_test))
# Match with candidates
candidate = ['Unknown1', 'Unknown2', 'Shishi', 'Unknown4', 'Bingbing', 'Feifei']
c_d = dict(zip(candidate, dist))
cd_sorted = sorted(c_d.items(), key=lambda x: x[1])
print("The person is:", cd_sorted[0][0])
```
To run the script, use the following command in your terminal:
```bash
python girl-face-rec.py 1.dat 2.dat ./candidate-faces test1.jpg
```
Make sure to replace `1.dat` and `2.dat` with the actual file names of your models.
The results are usually accurate, especially when the test image closely matches one of the candidates. However, in cases where the image is slightly off (like a side view or with shadows), the system may misidentify the person. This shows that while machines are powerful, they still require human input to improve accuracy.
If you're interested, you can experiment further by adding more images per person and averaging the distances to increase accuracy. Face recognition is a fascinating field, and with a little effort, you can build your own system in no time.