El Bruno

Hi!

Yesterday I explained how to write a couple of lines in Python to perform live face detection in a webcam feed [Post]. Check the resources section to find more about the tools I’m using.

Today, I’ll add some more code to perform face recognition. And as usual, I’ll work with my kids to test this out. I’ll start adding 2 face encodings for Valentino and myself. The code is simple enough, and I use a simple 300×300 head-shot photo to train and get the face encoding.

	def LoadFaces():
	bruno_image = face_recognition.load_image_file("d:\Faces\Bruno1.jpg")
	bruno_face_encoding = face_recognition.face_encodings(bruno_image)[0]
	valentino_image = face_recognition.load_image_file("d:\Faces\Valen1.jpg")
	valentino_face_encoding = face_recognition.face_encodings(valentino_image)[0]
	known_face_encodings = [
	bruno_face_encoding,
	valentino_face_encoding
	]
	known_face_names = [
	"Bruno",
	"Valentino"
	]
	return known_face_encodings, known_face_names;

view raw FaceDetectionLoadFaces.py hosted with ❤ by GitHub

The previous function returns an set of arrays with the face encodings and the face names. In the complete file, I’ll use this to analyze the camera frame (line 31) and later to check the matches for faces (lines 34 * 36)

	import face_recognition
	import cv2
	import numpy as np
	def LoadFaces():
	bruno_image = face_recognition.load_image_file("d:\Faces\Bruno1.jpg")
	bruno_face_encoding = face_recognition.face_encodings(bruno_image)[0]
	valentino_image = face_recognition.load_image_file("d:\Faces\Valen1.jpg")
	valentino_face_encoding = face_recognition.face_encodings(valentino_image)[0]
	known_face_encodings = [
	bruno_face_encoding,
	valentino_face_encoding
	]
	known_face_names = [
	"Bruno",
	"Valentino"
	]
	return known_face_encodings, known_face_names;
	video_capture = cv2.VideoCapture(0)
	known_face_encodings, known_face_names = LoadFaces()
	while True:
	ret, frame = video_capture.read()
	rgb_frame = frame[:, :, ::-1]
	face_locations = face_recognition.face_locations(rgb_frame)
	face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
	for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
	matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
	name = "Unknown"
	face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
	best_match_index = np.argmin(face_distances)
	if matches[best_match_index]:
	name = known_face_names[best_match_index]
	cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
	cv2.rectangle(frame, (left, bottom – 25), (right, bottom), (0, 0, 255), cv2.FILLED)
	font = cv2.FONT_HERSHEY_COMPLEX_SMALL
	cv2.putText(frame, name, (left + 6, bottom – 6), font, 0.7, (255, 255, 255), 1)
	cv2.imshow('Video', frame)
	if cv2.waitKey(1) & 0xFF == ord('q'):
	break
	video_capture.release()
	cv2.destroyAllWindows()

view raw FaceRecognitionSample.py hosted with ❤ by GitHub

Last lines are cosmetic to mostly draw the frames for the detected faces, and show the names.

The complete project is available here https://github.com/elbruno/Blog/tree/master/20190521%20Python%20FaceRecognition

Happy Coding!

Greetings @ Burlington

El Bruno

Resources

• Anaconda Distribution, https://www.anaconda.com/distribution/
• Visual Studio Code, https://code.visualstudio.com/
• Adam Geitgey, https://github.com/ageitgey/face_recognition

My Posts

• Let’s do some Face Recognition with 20 lines in Python