Unlock Powerful Face Recognition with Python’s face_recognition Library

face_recognition is a powerful, simple, and easy‑to‑use open‑source face recognition project that comes with comprehensive documentation and example applications, and it is compatible with Raspberry Pi.

The project’s source code is available at https://github.com/ageitgey/face_recognition.

Install the library via pip install face_recognition and verify the installation with a quick test.

1. Locate faces in an image

def demoFunc():
    '''
    在一张包含人脸的图片中圈出来人脸
    '''
    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()

Resulting detection image:

2. Crop each detected face and save locally

def demoFunc():
    '''
    图片中人脸截图保存
    '''
    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()

Cropped face results:

3. Encode each face into a 128‑dimensional vector

def demoFunc():
    '''
    将图片中的每张人脸编码成一个128维长度的向量
    '''
    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)  # 将单个人脸数据转化为一个128维的向量
    for one in face_encodings:
        print('one: ', one)

Example vector output (using a picture of Jackie Chan):

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

def demoFunc(one_pic='c1.jpg', two_pic='c2.jpg'):
    '''
    给定两张图片，判断是否是同一个人
    '''
    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]

Comparison result image:

5. Detect facial landmarks and annotate them

def demoFunc(pic_path='cl.jpg'):
    '''
    脸部关键点识别、标注
    '''
    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()

Landmark visualization results:

6. Apply virtual makeup using facial landmarks

def demoFunc(pic_path="haiwang.jpg"):
    '''
    化妆
    '''
    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()

Virtual‑makeup results:

7. Build a simple face‑recognition application

def faceRecognitionDemo(picDir='data/', test_pic='test.png'):
    '''
    基于 face_recognition 构建人脸识别模块
    '''
    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)

Application output screenshot:

The face_recognition library essentially converts each face image into a 128‑dimensional feature vector; similarity between vectors determines identity, and the process can be extended with machine‑learning classifiers such as SVM, Random Forest, or Gradient Boosting for more robust recognition.

Original article: https://yishuihancheng.blog.csdn.net/article/details/102831117