如何计算人脸识别系统的准确率？

Question

I am new to Biometric Evaluation and I wish to plot the ROC curve, CMC curve, and Genuine Vs Imposter Distribution. I trained the model on my dataset based on https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/ . If I give a test image, it is working correctly. But, I do not know how to get a genuine and imposter score based on this method for the entire Test Dataset.

Answer 1

All state-of-the-art models such as VGG-Face, FaceNet or DeepFace tested on LFW (Labeled Faces in the Wild) data set. Luckily, scikit learn offers this data set as an out-of-the-box function.

from sklearn.datasets import fetch_lfw_pairs
fetch_lfw_pairs = fetch_lfw_pairs(subset = 'test', color = True, resize = 1)
pairs = fetch_lfw_pairs.pairs
labels = fetch_lfw_pairs.target

Now, you should test each pair with your model.

predictions = []
for i in range(0, pairs.shape[0]):
   pair = pairs[i]
   img1 = pair[0]
   img2 = pair[1]
   prediction = verify(img1, img2) #this should return 1 for same person, 0 for different persons.
   predictions.append(prediction)

Then, you should compare predictions and labels.

from sklearn.metrics import accuracy_score
score = accuracy_score(labels, predictions)

Besides, you can calculate some other metrics

from sklearn.metrics import precision_score, recall_score, f1_score

precision = precision_score(actuals, predictions)
recall = recall_score(actuals, predictions)
f1 = f1_score(actuals, predictions)