train_model.py

from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
import pickle

args = {
	"embeddings":"output/embeddings.pickle",
	"recognizer":"output/recognizer.pickle",
	"le":"output/le.pickle",
}

# load the face embeddings
print("[INFO] loading face embeddings...")
data = pickle.loads(open(args["embeddings"], "rb").read())

# encode the labels
print("[INFO] encoding labels...")
le = LabelEncoder()
labels = le.fit_transform(data["names"])

# train the model used to accept the 128-d embeddings of the face and
# then produce the actual face recognition
print("[INFO] training model...")
recognizer = SVC(kernel="linear", probability=True)
recognizer.fit(data["embeddings"], labels)

# write the actual face recognition model to disk
f = open(args["recognizer"], "wb")
f.write(pickle.dumps(recognizer))
f.close()

# write the label encoder to disk
f = open(args["le"], "wb")
f.write(pickle.dumps(le))
f.close()