#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (4/N)

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Face Recognition and Face Detection series in Python

  1. Detecting Faces with 20 lines in Python
  2. Face Recognition with 20 lines in Python
  3. Detecting Facial Features with 20 lines in Python
  4. Facial Features and Face Recognition with 20 lines in Python
  5. Performance improvements with code
  6. Resize the camera input with OpenCV
  7. Working with Haar Cascades and OpenCV
  8. Detect and blur faces 😁 using haar cascades
  9. Detect and blur faces 😁 using DNN

Hi!

Quick post today. I’ll pickup yesterday demo, showing the facial features and adding Face Recognition on top of that. In other words, we’ll move from this

To this

With a couple of extra lines for face recognition

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_landmarks_list = face_recognition.face_landmarks(rgb_frame)
for face_landmarks in face_landmarks_list:
for facial_feature in face_landmarks.keys():
pts = np.array([face_landmarks[facial_feature]], np.int32)
pts = pts.reshape((–1,1,2))
cv2.polylines(frame, [pts], False, (0,255,0))
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), (right, bottom), (0, 255, 0), 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 FaceRecognitionDetecFacialFeaturesAndFaceRecog.py hosted with ❤ by GitHub

There is some room for performance improvement, so I’ll focus on this in next posts.

The complete project is available here https://github.com/elbruno/Blog/tree/master/20190528%20Python%20FaceRecognition

Happy Coding!

Greetings @ Burlington

El Bruno

Resources

My Posts

  1. Detecting Faces with 20 lines in Python
  2. Face Recognition with 20 lines in Python
  3. Detecting Facial Features with 20 lines in Python

7 comments

  1. I hate to break it to you Amigo, but this is closer to 40 lines 🤣🤣🤣.

    Neat stuff, we should get this into all the inno studios. See you in Milan next week.

    Liked by 1 person

    Reply
  2. Pingback: #VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (5/N) – El Bruno
  3. Pingback: #VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (6/N) – El Bruno
  4. Pingback: #VSCode – 20 lines to display a webcam camera feed with #Python using #OpenCV – El Bruno

  5. Hi Bruno,
    I am a student who is currently doing facial recognition for my project. And I have followed your guide from this website https://elbruno.com/2019/05/30/vscode-lets-do-some-facerecognition-with-20-lines-in-python-4-n/ However, there was some issues with the accuracy of the face recognition. I was wondering if there is a way to add more image to the data base instead using only one picture to increase the dataset ? Below is an example of the code i used to retrieve the image. May i know how do i retrieve a set of image instead of a single image for better accuracy ?

    JIMMY_image = face_recognition.load_image_file(r”/Users/jimmy/Desktop/Face/JIMMY/JIMMY.jpeg”)

    Like

    Reply

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