Using the face_recognition Python Library for Face Detection, Landmark Identification, and Simple Applications

The face_recognition library is a powerful, easy‑to‑use open‑source project for face recognition that includes complete documentation and works on platforms such as Raspberry Pi.

Install the library with pip install face_recognition and verify the installation using a short test script that loads an image and displays detected faces.

1. Locate faces in an image

def demoFunc():
    '''
    Detect faces in a picture and draw rectangles around them
    '''
    image = face_recognition.load_image_file("test.jpg")
    face_locations = face_recognition.face_locations(image)
    for one in face_locations:
        y0, x1, y1, x0 = one
        cv2.rectangle(image, pt1=(x0, y0), pt2=(x1, y1), color=(0, 0, 255), thickness=3)
    cv2.imshow('aaa', image)
    if cv2.waitKey(0) & 0xFF == ord('q'):
        cv2.destroyAllWindows()

2. Crop and save each detected face

def demoFunc():
    '''
    Crop each face from the image and save it locally
    '''
    img = cv2.imread("test.jpg")
    image = face_recognition.load_image_file("test.jpg")
    face_locations = face_recognition.face_locations(image)  # (top, right, bottom, left)
    for i in range(len(face_locations)):
        y0, x1, y1, x0 = face_locations[i]
        cropped = img.crop((x0, y0, x1, y1))  # (left, upper, right, lower)
        cropped.save(str(i) + "_.jpg")
        cropped.show()

3. Encode each face into a 128‑dimensional vector

def demoFunc():
    '''
    Convert each face in the picture to a 128‑dimensional vector
    '''
    image = face_recognition.load_image_file("cl.jpg")
    face_locations = face_recognition.face_locations(image)  # (top, right, bottom, left)
    face_encodings = face_recognition.face_encodings(image, face_locations)  # 128‑D vectors
    for one in face_encodings:
        print('one: ', one)

The core principle of face_recognition is to map each face image to a 128‑dimensional feature vector; comparing two faces then reduces to measuring the similarity between their vectors.

4. Compare two images to determine if they belong to the same person

def demoFunc(one_pic='c1.jpg', two_pic='c2.jpg'):
    '''
    Determine whether two pictures show the same person
    '''
    chenglong = face_recognition.load_image_file(one_pic)
    unknown_image = face_recognition.load_image_file(two_pic)
    biden_encoding = face_recognition.face_encodings(chenglong)[0]
    unknown_encoding = face_recognition.face_encodings(unknown_image)[0]
    results = face_recognition.compare_faces([biden_encoding], unknown_encoding)
    print('results: ', results)
    return results[0]

5. Detect facial landmarks and annotate them

def demoFunc(pic_path='cl.jpg'):
    '''
    Detect and annotate facial landmarks
    '''
    image = face_recognition.load_image_file(pic_path)
    face_landmarks_list = face_recognition.face_landmarks(image)
    print("I found {} face(s) in this photograph.".format(len(face_landmarks_list)))
    pil_image = Image.fromarray(image)
    d = ImageDraw.Draw(pil_image)
    for face_landmarks in face_landmarks_list:
        for facial_feature in face_landmarks.keys():
            print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))
        for facial_feature in face_landmarks.keys():
            d.line(face_landmarks[facial_feature], width=5)
    pil_image.show()

6. Apply virtual makeup using the detected landmarks

def demoFunc(pic_path='haiwang.jpg'):
    '''
    Apply makeup based on facial landmarks
    '''
    image = face_recognition.load_image_file(pic_path)
    face_landmarks_list = face_recognition.face_landmarks(image)
    pil_image = Image.fromarray(image)
    for face_landmarks in face_landmarks_list:
        demo = ImageDraw.Draw(pil_image, 'RGBA')
        demo.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128))
        demo.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128))
        demo.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=2)
        demo.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=2)
        demo.polygon(face_landmarks['top_lip'], fill=(150, 0, 0, 128))
        demo.polygon(face_landmarks['bottom_lip'], fill=(150, 0, 0, 128))
        demo.line(face_landmarks['top_lip'], fill=(150, 0, 0, 64), width=2)
        demo.line(face_landmarks['bottom_lip'], fill=(150, 0, 0, 64), width=2)
        demo.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))
        demo.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))
        demo.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]], fill=(0, 0, 0, 110), width=2)
        demo.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]], fill=(0, 0, 0, 110), width=2)
    pil_image.show()

7. Build a simple face‑recognition application by combining the above functions

def faceRecognitionDemo(picDir='data/', test_pic='test.png'):
    '''
    Simple face‑recognition module based on face_recognition
    '''
    pic_list = os.listdir(picDir)
    for one_pic in pic_list:
        one_pic_path = picDir + one_pic
        one_res = demo6(one_pic=one_pic_path, two_pic=test_pic)
        one_name = one_pic.split('.')[0].strip()
        if one_res:
            print('This Person is: ', one_name)
            break
        else:
            print('This Person is not: ', one_name)

The article also notes that after obtaining 128‑D face embeddings, the recognition task can be turned into a classic machine‑learning classification problem using models such as SVM, Random Forest, or Gradient Boosted Trees.

Images throughout the original tutorial illustrate the results of each step, from face bounding boxes to landmark visualizations and virtual‑makeup effects.