人脸识别分为两大类：
Face Verification指的是，这张脸是不是xxx？
Face Recognition指的是，这张脸是谁？
FaceNet是一种神经网络，把一张人脸照片编码成为128组向量，通过比较两张照片的128组向量来确定是不是同一个人。
本实例中使用训练好的ConvNet激活函数，应用的是channel first的形式（m, nc, nw, nh）
将人脸照片编码为128维向量
FaceNet模型需要大量数据和时间训练，本程序中直接使用训练好的FaceNet初级模型，这个Inception神经网络架构依照Szegedy et al
该模型输入m张96*96*3的人脸照片，输出向量（m,128）
神经网络最后一层使用128个神经元，输出128为向量
模型训练目标：
如果两张图片是一个人，距离非常小。否则，距离很大。
第一步、定义Triplet loss
Triplet loss方法的原理是push两个相同人的照片在一起(Anchor and Positive)，pull不同人的照片分开(Anchor, Negative)
损失函数：
实现步骤：
代码：
def triplet_loss(y_true, y_pred, alpha = 0.2):
"""
Implementation of the triplet loss as defined by formula (3)
Arguments:
y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor images, of shape (None, 128)
positive -- the encodings for the positive images, of shape (None, 128)
negative -- the encodings for the negative images, of shape (None, 128)
Returns:
loss -- real number, value of the loss
"""
anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
### START CODE HERE ### (≈ 4 lines)
# Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)),axis=-1)
# Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)),axis=-1)
# Step 3: subtract the two previous distances and add alpha.
basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
# Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
loss = tf.reduce_sum(tf.maximum(basic_loss, 0.))
第二步、加载训练好的模型和Triplet loss
由于FaceNet训练量太大，直接加载训练好的模型：
FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
load_weights_from_FaceNet(FRmodel)
Face Varification实现
第三步、加载数据库数据
database = {}
database["danielle"] = img_to_encoding("images/danielle.png", FRmodel)
database["younes"] = img_to_encoding("images/younes.jpg", FRmodel)
database["tian"] = img_to_encoding("images/tian.jpg", FRmodel)
database["andrew"] = img_to_encoding("images/andrew.jpg", FRmodel)
database["kian"] = img_to_encoding("images/kian.jpg", FRmodel)
database["dan"] = img_to_encoding("images/dan.jpg", FRmodel)
database["sebastiano"] = img_to_encoding("images/sebastiano.jpg", FRmodel)
database["bertrand"] = img_to_encoding("images/bertrand.jpg", FRmodel)
database["kevin"] = img_to_encoding("images/kevin.jpg", FRmodel)
database["felix"] = img_to_encoding("images/felix.jpg", FRmodel)
database["benoit"] = img_to_encoding("images/benoit.jpg", FRmodel)
database["arnaud"] = img_to_encoding("images/arnaud.jpg", FRmodel)
第四步、
对输入照片进行编码（输出128维向量）
判断输入照片与数据库里的照片distance
如果distance小于0.7，则开门
代码实现：
def verify(image_path, identity, database, model):
"""
Function that verifies if the person on the "image_path" image is "identity".
Arguments:
image_path -- path to an image
identity -- string, name of the person you'd like to verify the identity. Has to be a resident of the Happy house.
database -- python dictionary mapping names of allowed people's names (strings) to their encodings (vectors).
model -- your Inception model instance in Keras
Returns:
dist -- distance between the image_path and the image of "identity" in the database.
door_open -- True, if the door should open. False otherwise.
"""
### START CODE HERE ###
# Step 1: Compute the encoding for the image. Use img_to_encoding() see example above. (≈ 1 line)
encoding = img_to_encoding(image_path, model)
# Step 2: Compute distance with identity's image (≈ 1 line)
dist = np.linalg.norm(encoding-database[identity])
# Step 3: Open the door if dist < 0.7, else don't open (≈ 3 lines)
if dist < 0.7:
print("It's " + str(identity) + ", welcome home!")
door_open = True
else:
print("It's not " + str(identity) + ", please go away")
door_open = False
Face Recognition实现
1.编码目标图像，输出128维向量
2.找到数据库里距离目标图像distance最小的图像
a.把变量min_dist初始化为一个足够大的值，比如100
b.循环遍历数据库里的图像与目标图像的距离，找出小于min_dist距离的图像，遍历后找出距离目标图像最小的图像
代码实现：
def who_is_it(image_path, database, model):
"""
Implements face recognition for the happy house by finding who is the person on the image_path image.
Arguments:
image_path -- path to an image
database -- database containing image encodings along with the name of the person on the image
model -- your Inception model instance in Keras
Returns:
min_dist -- the minimum distance between image_path encoding and the encodings from the database
identity -- string, the name prediction for the person on image_path
"""
### START CODE HERE ###
## Step 1: Compute the target "encoding" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)
encoding = img_to_encoding(image_path, model)
## Step 2: Find the closest encoding ##
# Initialize "min_dist" to a large value, say 100 (≈1 line)
min_dist = 100
# Loop over the database dictionary's names and encodings.
for (name, db_enc) in database.items():
# Compute L2 distance between the target "encoding" and the current "emb" from the database. (≈ 1 line)
dist = np.linalg.norm(encoding-db_enc)
# If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)
if dist < min_dist:
min_dist = dist
identity = name
### END CODE HERE ###
if min_dist > 0.7:
print("Not in the database.")
else:
print ("it's " + str(identity) + ", the distance is " + str(min_dist))
return min_dist, identity