Face Recognition Based on FaceNet

1.导入包
from keras.models import Sequential 
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
from keras.layers.merge import Concatenate
from keras.layers.core import Lambda, Flatten, Dense
from keras.initializers import glorot_uniform
from keras.engine.topology import Layer
from keras import backend as K
K.set_image_data_fromat("channels_first")
import cv2
import os
import numpy as np
from numpy import genfromtxt
import pandas as pd
import tensorflow as tf
from fr_utils import *
from inception_blocks_v2 import *


%matplotlib online
%load_ext autoreload
%autoreload 2


np.set_printoptions(threshold=np.nan)


2. Create model
FRmodel = faceRecoModel(input_shape=(3, 96, 96))
3 .Define the loss function
def triplet_loss(y_true, y_pred, alpha = 0.2): 4.Load
the trained model
FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
load_weights_from_FaceNet(FRmodel)
5. Build the database
database = {}
database["Dai Lin"] = img_to_encoding("images/DL.jpg", FRmodel)
database["Wang Dalei"] = img_to_encoding("images/WDL.jpg", FRmodel)
database["刘彬彬"] = img_to_encoding("images/LBB.jpg", FRmodel)
6.定义Face Varification
def verify(image_path, identity, database, model):
7.执行Face Varification
verify("images/camera_5.jpg", "王大雷", database, FRmodel)
8.定义Face Recognition
def who_is_it(image_path, database, model):
9.执行Face Recognition
who_is_it("images/camera_0.jpg", database, FRmodel)