Master the face recognition library 'InsightFace', learn about face authentication and the structure of AI models (1)

What is Face Authentication?

Face authentication is the process of verifying whether a person is who they claim to be. In a face authentication system, the provided facial image is compared with pre-registered images to confirm the identity of the person. Systems unlocking doors through face recognition are becoming commonplace.

Let’s Try Using InsightFace (Sample Code Included)

Let's try using InsightFace. Install it using the pip command. After the installation is complete, try running the code below. This code takes two images from the camera with a one-second interval, detects the face from the images, and extracts face features (embedding). By comparing these vectors, you can calculate face similarity. Try running the code below, and the similarity of the faces displayed will be shown.

import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
import time

# １枚目の画像を撮影
cap = cv2.VideoCapture(0)
img_1 = cap.read()[1]
h, w = img_1.shape[:2]
# rgbへ変換
img_1 = cv2.cvtColor(img_1, cv2.COLOR_BGR2RGB)

# １秒末
time.sleep(1)

# 2枚目の画像を撮影
cap = cv2.VideoCapture(0)
img_2 = cap.read()[1]
h, w = img_2.shape[:2]
# rgbへ変換
img_2 = cv2.cvtColor(img_2, cv2.COLOR_BGR2RGB)

app = FaceAnalysis()
app.prepare(ctx_id=0, det_size=(640, 640))
# 1枚目の画像から顔を検出
faces = app.get(img_1)
if len(faces) == 0:
    print("顔が検出されませんでした。終了します。")
    exit()
face_1 = faces[0]
# 2枚目の画像から顔を検出
faces = app.get(img_2)
if len(faces) == 0:
    print("顔が検出されませんでした。終了します。")
    exit()
face_2 = faces[0]

# Compare face embeddings
embedding_1, embedding_2 = face_1.embedding, face_2.embedding
similarity = np.dot(embedding_1, embedding_2) / (
    np.linalg.norm(embedding_1) * np.linalg.norm(embedding_2)
)
print(f"１枚目と２枚目に映った顔の類似度は: {similarity}")
    

With just this code, you’ve implemented face authentication. InsightFace is a library that allows easy implementation of face authentication. However, in this course, the goal is not just to use InsightFace but to dissect it and learn how AI is implemented. If your goal is just to quickly use face authentication, the above code is sufficient and you might not need to read further. The content hereafter is aimed at intermediate programmers and may include complex content.

Let’s Look Inside the InsightFace Code

Once you have installed InsightFace, open the installation folder. You can confirm the installation folder with,

pip show insightface
    

With that, you should be able to check the source code. You can also confirm the same on the InsightFace’s GitHub repository, which should be identical to the content in the installation folder.

class FaceAnalysis:
    def __init__(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs):
        onnxruntime.set_default_logger_severity(3)
        self.models = {}
        self.model_dir = ensure_available('models', name, root=root)
        onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx'))
        onnx_files = sorted(onnx_files)
        for onnx_file in onnx_files:
            model = model_zoo.get_model(onnx_file, **kwargs)
            if model is None:
                print('model not recognized:', onnx_file)
            elif allowed_modules is not None and model.taskname not in allowed_modules:
                print('model ignore:', onnx_file, model.taskname)
                del model
            elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules):
                print('find model:', onnx_file, model.taskname, model.input_shape, model.input_mean, model.input_std)
                self.models[model.taskname] = model
            else:
                print('duplicated model task type, ignore:', onnx_file, model.taskname)
                del model
        assert 'detection' in self.models
        self.det_model = self.models['detection']

    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)):
        self.det_thresh = det_thresh
        assert det_size is not None
        print('set det-size:', det_size)
        self.det_size = det_size
        for taskname, model in self.models.items():
            if taskname == 'detection':
                model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh)
            else:
                model.prepare(ctx_id)
    // omitted below
    

By the Way, What is ONNX?

ONNX stands for Open Neural Network Exchange. It’s a common format designed to easily share and port models between different machine learning frameworks and tools. Models trained in popular machine learning frameworks like PyTorch, TensorFlow, and MXNet can be exported in the ONNX format. This makes it easy to migrate models to different frameworks for inference. As found in our investigation, InsightFace also handles AI models in the ONNX format.

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