deepface是一个Python轻量级人脸识别和人脸属性分析（年龄、性别、情感和种族）框架，提供非常简单的接口就可以实现各种人脸识别算法的应用。deepface官方仓库为deepface。deepface提供了多种模型，模型下载地址为deepface_models。

安装方式： pip install deepface -i https://pypi.tuna.tsinghua.edu.cn/simple

deepface主要提供以下人脸识别算法，具体对应接口为：

DeepFace.verify：人脸验证DeepFace.find：人脸识别DeepFace.analyze：人脸属性分析DeepFace.detectFace：人脸检测DeepFace.represent：人脸特征提取DeepFace.stream：人脸实时分析

总体而言，这个项目的人脸识别模型识别效果还行，但是离工程应用还是有一定的距离，不过还是非常推荐学习该库内部代码。

某些网站会判定本文人脸图片违规，这是网站识别算法自身问题。

本文所有算法展示效果和代码见：

github: Python-Study-Notes

此外可以看一看另外一个人脸识别库，功能更加齐全：[深度学习] Python人脸识别库face_recognition使用教程

文章目录

0 数据准备1 人脸验证DeepFace.verify2 人脸识别DeepFace.find3 人脸属性分析DeepFace.analyze4 人脸检测DeepFace.detectFace5 人脸特征提取DeepFace.represent6 参考

0 数据准备

# deep库的导入就一行代码

from deepface import DeepFace

import matplotlib.pyplot as plt

from PIL import Image, ImageDraw

import os

import cv2

import numpy as np

所使用的数据集为网络明星图片，共五个明星，每个明星三张人脸，数据集的路径如下：

root

├── images

│ ├── baijingting

│ │ ├── 0000.jpg

│ │ ├── 0001.jpg

│ ├── jiangwei

│ │ ├── 0000.jpg

│

├── code

数据展示结果如下：

# --- 展示图片

def show_img(imgs: list, img_names: list) -> None:

imgs_count = len(imgs)

for i in range(imgs_count):

ax = plt.subplot(1, imgs_count, i+1)

ax.imshow(imgs[i])

ax.set_title(img_names[i])

ax.set_xticks([])

ax.set_yticks([])

plt.tight_layout(h_pad=3)

plt.show()

img_path = "images"

for person_dir in os.listdir(img_path):

imgs = []

img_names = []

for file in os.listdir(os.path.join(img_path, person_dir)):

imgs.append(Image.open(os.path.join(img_path, person_dir, file)))

img_names.append(person_dir + '/' + file)

show_img(imgs, img_names)

)

)

1 人脸验证DeepFace.verify

此函数用于验证图像对是同一个人还是不同的人。函数接口为：

verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True, normalization = 'base')

输入参数介绍：

img1_path：传递的图像路径、numpy数组（BGR）或based64编码图像

model_name：模型名，支持VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace，Ensemble等

distance_metric：度量标准，支持cosine, euclidean, euclidean_l2

model：构建deepface模型。每次调用verify函数都会重新建立人脸识别模型。可以选择传递预构建的人脸识别模型。如DeepFace.build_model('VGG-Face')构建模型

enforce_detection：如果在图像中检测不到任何人脸，则验证函数将返回异常。将此设置为False将不会出现此异常

detector_backend：人脸识别算法后端，支持retinaface, mtcnn, opencv, ssd，dlib

align：是否人脸对齐

prog_bar：启用或禁用进度条

normalization：人脸归一化的方式

输出结果介绍：

如果img1_path是输入一张人脸就是返回一个字典，如果输入列表则返回一个字典列表。具体参数如下：

verified：是否同一个人

distance：人脸距离，越小越相似

max_threshold_to_verify：判断为同一个人的阈值

model: 所用模型

similarity_metric: 相似性度量标准

各识别模型的精度如下，LFW和YTF都是小型数据集。Human-beings表示人类识别精度。

ModelLFW ScoreYTF ScoreFacenet51299.65%-SFace99.60%-ArcFace99.41%-Dlib99.38 %-Facenet99.20%-VGG-Face98.78%97.40%Human-beings97.53%-OpenFace93.80%-DeepID-97.05%

demo1

# 模型名

models_name = ["VGG-Face", "Facenet", "Facenet512", "OpenFace",

"DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", 'Ensemble']

model_name = models_name[5]

result = DeepFace.verify(img1_path="images/baijingting/0001.jpg",

img2_path="images/pengyuyan/0001.jpg",

model_name=model_name)

# 展示结果，两个人不是同一个人

print(result)

1/1 [==============================] - 0s 170ms/step

1/1 [==============================] - 0s 20ms/step

{'verified': False, 'distance': 0.0751386867894902, 'threshold': 0.015, 'model': 'DeepID', 'detector_backend': 'opencv', 'similarity_metric': 'cosine'}

demo2

models_name = ["VGG-Face", "Facenet", "Facenet512", "OpenFace",

"DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", 'Ensemble']

# 提前加载模型，避免重复加载

model_name = models_name[1]

# 创建模型

model = DeepFace.build_model(model_name)

# 列表中每一个子项表示用于对比的图像

img_paths = [["images/baijingting/0000.jpg", "images/baijingting/0001.jpg"],

["images/baijingting/0000.jpg", "images/zhaoliying/0001.jpg"]]

# 度量标准

metrics = ["cosine", "euclidean", "euclidean_l2"]

results = DeepFace.verify(img_paths,

model_name=model_name,

model=model,

distance_metric=metrics[2],

prog_bar=False)

# 展示结果

for result in results.items():

print(result)

1/1 [==============================] - 2s 2s/step

1/1 [==============================] - 0s 52ms/step

1/1 [==============================] - 0s 55ms/step

1/1 [==============================] - 0s 66ms/step

('pair_1', {'verified': True, 'distance': 0.6328494898310356, 'threshold': 0.8, 'model': 'Facenet', 'detector_backend': 'opencv', 'similarity_metric': 'euclidean_l2'})

('pair_2', {'verified': False, 'distance': 1.1700473293978308, 'threshold': 0.8, 'model': 'Facenet', 'detector_backend': 'opencv', 'similarity_metric': 'euclidean_l2'})

2 人脸识别DeepFace.find

此函数用于从数据集中检索当前人脸相似的图片。函数接口为：

find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True, normalization = 'base', silent=False):

输入参数和verify差不多，主要多了人脸检索库路径地址：

db_path：检索库路径，

silent: 是否静默显示数据，

输出结果介绍：

一个包含相似图像的pandas dataframe数据体，包括图像路径和距离值，

models_name = ["VGG-Face", "Facenet", "Facenet512", "OpenFace",

"DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", 'Ensemble']

# db_path是库文件地址

# 第一次会提取各个图像的特征，并保存到本地pkl文件以供下次直接调用

result = DeepFace.find(img_path="images/baijingting/0000.jpg",

db_path="images", model_name=models_name[1])

1/1 [==============================] - 0s 55ms/step

1/1 [==============================] - 0s 64ms/step

1/1 [==============================] - 0s 63ms/step

1/1 [==============================] - 0s 61ms/step

1/1 [==============================] - 0s 64ms/step

1/1 [==============================] - 0s 58ms/step

1/1 [==============================] - 0s 55ms/step

1/1 [==============================] - 0s 65ms/step

1/1 [==============================] - 0s 59ms/step

1/1 [==============================] - 0s 55ms/step

1/1 [==============================] - 0s 51ms/step

1/1 [==============================] - 0s 52ms/step

1/1 [==============================] - 0s 53ms/step

1/1 [==============================] - 0s 52ms/step

1/1 [==============================] - 0s 55ms/step

Representations stored in images / representations_facenet.pkl file. Please delete this file when you add new identities in your database.

1/1 [==============================] - 0s 56ms/step

find function lasts 3.254298448562622 seconds

# 展示结果，第一个是识别图像本身，后面两个是相似图片

print(result)

identity Facenet_cosine

0 images\baijingting/0000.jpg -2.220446e-16

1 images\baijingting/0001.jpg 2.002492e-01

2 images\baijingting/0002.jpg 2.328966e-01

3 人脸属性分析DeepFace.analyze

此函数用于分析当前人脸的面部属性，包括年龄，性别，面部表情（包括愤怒、恐惧、正常、悲伤、厌恶、快乐和惊讶），种族（包括亚洲人、白人、中东人、印度人、拉丁裔和黑人）。函数接口为：

analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models = None, enforce_detection = True, detector_backend = 'opencv', prog_bar = True)

输入参数和verify差不多，主要多了属性设置actions：

actions：识别属性，包括age, gender, emotion, race

输出结果介绍：

如果img_path是输入一张人脸就是返回一个字典，如果输入列表则返回一个字典列表。具体参数如下：

region：人脸坐标，wywh格式

age：年龄

gender：性别

dominant_emotion: 主导情绪，也就是情绪识别结果

emotion：各个情绪度量值，值越大表示越倾向

dominant_race：种族结果

race：各个种族度量值

# 输入检测图像，这里只识别情绪，因为其他模型实在太大了，下载下来要很久。

result = DeepFace.analyze(img_path = "images/jiangwen/0000.jpg", actions = ['emotion'])

print(result)

1/1 [==============================] - 0s 113ms/step

{'emotion': {'angry': 2.147514166495057e-06, 'disgust': 3.124029827739067e-14, 'fear': 1.990160924947304e-06, 'happy': 99.9697208404541, 'sad': 1.9864262412738753e-05, 'surprise': 0.01537421194370836, 'neutral': 0.014887277211528271}, 'dominant_emotion': 'happy', 'region': {'x': 198, 'y': 34, 'w': 185, 'h': 185}}

数据可视化看看结果

im = Image.open( "images/jiangwen/0000.jpg")

# 坐标位置

x,y,w,h = result['region']['x'],result['region']['y'],result['region']['w'],result['region']['h']

draw = ImageDraw.Draw(im)

# 画框

draw.rectangle((x,y,x+w,y+h), outline="red", width=3)

print("表情：{}".format(result["dominant_emotion"]))

show_img([im],["jiangwen"])

表情：happy

4 人脸检测DeepFace.detectFace

此函数用于检测人脸，如果图像中有多个人脸只会返回一个，函数接口为：

detectFace(img_path, target_size = (224, 224), detector_backend = 'opencv', enforce_detection = True, align = True)

输入参数和verify差不多，主要多了可以设置返回图像的尺寸的参数target_size，输出返回一张RGB的numpy数组图像

result = DeepFace.detectFace(img_path = "images/zhangziyi/0000.jpg",align = True)

print(result.shape)

show_img([result],["zhangziyi"])

(224, 224, 3)

# 不进行人脸对齐

result = DeepFace.detectFace(img_path = "images/zhangziyi/0000.jpg",align = False)

print(result.shape)

show_img([result],["zhangziyi"])

(224, 224, 3)

5 人脸特征提取DeepFace.represent

该函数用于将面部图像表示为特征向量，函数接口为：

represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, normalization = 'base')

输入参数和verify差不多。输出返回图像特征多维向量，特征向量的维度根据模型而变化。

models_name = ["VGG-Face", "Facenet", "Facenet512", "OpenFace",

"DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", 'Ensemble']

result = DeepFace.represent(img_path="images/baijingting/0000.jpg", model_name=models_name[1])

print("特征维度为：{}".format(len(result)))

1/1 [==============================] - 0s 61ms/step

特征维度为：128

当然提取特征可以自己计算距离，设置阈值。示例如下。

# 计算l2距离

def l2_distance(input1: np.ndarray, input2: np.ndarray) -> float:

# 手动计算 np.sqrt(np.sum((result1- result2)**2))

return np.linalg.norm(input1-input2)

# 计算l1距离

def l1_distance(input1: np.ndarray, input2: np.ndarray) -> float:

# 手动计算 np.sum(abs(input1-input2))

return np.linalg.norm(input1-input2, ord=1)

# 计算余弦距离

def IP_distance(input1: np.ndarray, input2: np.ndarray) -> float:

return 1 - np.dot(input1, input2)/np.linalg.norm(input1)/np.linalg.norm(input2)

models_name = ["VGG-Face", "Facenet", "Facenet512", "OpenFace",

"DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", 'Ensemble']

# 提前加载模型，避免重复加载

model_name = models_name[1]

# 创建模型

model = DeepFace.build_model(model_name)

# res1和res3为同一个人

res1 = DeepFace.represent(

img_path="images/baijingting/0000.jpg", model_name=models_name[1], model=model)

res2 = DeepFace.represent(

img_path="images/zhangziyi/0000.jpg", model_name=models_name[1], model=model)

res3 = DeepFace.represent(

img_path="images/baijingting/0001.jpg", model_name=models_name[1], model=model)

# 转换为numpy类型

res1 = np.array(res1)

res2 = np.array(res2)

res3 = np.array(res3)

print("res1与res2的余弦距离为：{}".format(IP_distance(res1,res2)))

print("res1与res3的余弦距离为：{}".format(IP_distance(res1,res3)))

print("res1与res2的l2距离为：{}".format(l2_distance(res1,res2)))

print("res1与res3的l2距离为：{}".format(l2_distance(res1,res3)))

print("res1与res2的l1距离为：{}".format(l1_distance(res1,res2)))

print("res1与res3的l1距离为：{}".format(l1_distance(res1,res3)))

1/1 [==============================] - 0s 54ms/step

1/1 [==============================] - 0s 62ms/step

1/1 [==============================] - 0s 52ms/step

res1与res2的余弦距离为：0.6868675298615137

res1与res3的余弦距离为：0.2002492383897012

res1与res2的l2距离为：12.135816884638682

res1与res3的l2距离为：6.657409646028565

res1与res2的l1距离为：110.3180431430228

res1与res3的l1距离为：58.20380371063948

6 参考

deepfacedeepface_models[深度学习] Python人脸识别库face_recognition使用教程Python-Study-Notes