This is a paper proposing a siamese network for signature verification. Essentially, it can also be applied to other image matching tasks. The network, called SigNet, has the main branch to encode a signature image into a vector. With the signature pair, the encoded vectors are compared using contrastive loss function. The loss function value is treated as the score for the signature match.

The paper mentioned several sources of signature dataset. Including the CEDAR signature database, which has 1320 genuine and forgery signatures for 55 signers each, and GPDS synthetic signature database, which has hundreds to thousands of signers but need to sign a license to download.

As input to the siamese network, the signature images are resized to 155x220 pixels using bilinear interpolation. The paper suggested to invert the image to make the background pixel value 0 (i.e., white on black) and then normalize the pixel value with the standard deviation of the pixel values of that image.

The main branch of the siamese network is a deep convolutional network with kernel sizes 11x11 to 3x3 with ReLU activation and interleaved by pooling layers. The network is fed with two input images to output two vectors as a dimensionality reduction network. The Euclidean distance of the two vectors is then computed. It is assumed that matching signature pair will be close to each other. Hence the Euclidean distance is used as the similarity metric. The siamese network is regarded as a “weakly supervised metric learning”.

The loss function for the network is the contrastive loss (See the paper or this post). The formula is:

$L(s_1, s_2, y) = \alpha(1-y)D_w^2 + \beta y \max(0, m-D_w)^2$

where $$s_1,s_2$$ are the two images and $$y$$ is a binary indicator for whether the two images are matching signatures. The quantity $$m$$ is a margin and $$D_w$$ is the Euclidean distance. If the main branch of the siamese network is $$f()$$, which converts an image to a vector, then $$D_w = \lVert f(s_1) - f(s_2) \rVert_2$$. The contrastive loss defined above means when $$y=0$$ or the images match, we should expect a small $$D_w$$. But if $$y=1$$ or the images not match, we expect $$m-D_w$$ to be small, i.e., the Euclidean distance should close to or even beyond $$m$$. The coefficients $$\alpha,\beta$$ are to weight the contribution by positive and negative samples. The siamese network is trained to move the two vectors closer if they are matched pairs or move them away otherwise.

The paper mentioned a network architecture like the following, but using local response normalization (from AlexNet) instead of batch normalization. The reference code from the author is at https://github.com/sounakdey/SigNet/blob/master/SigNet_v1.py. It is rewritten into the following to use modern TensorFlow:

from tensorflow.keras import Sequential, Model, Input
from tensorflow.keras.layers import InputLayer, Conv2D, BatchNormalization, \
Dense, Lambda
from tensorflow.keras.regularizers import L2
import tensorflow.keras.backend as K

IMAGE_SHAPE=(155,220,3)

def make_base_signet():
seq = Sequential([
InputLayer(input_shape=IMAGE_SHAPE),

Conv2D(96, 11, strides=4, activation="relu", name="conv1_1",
kernel_initializer="glorot_uniform", data_format="channels_last"),
BatchNormalization(axis=-1, momentum=0.9, epsilon=1e-6), # feature normalization
MaxPool2D(pool_size=(3,3), strides=(2,2)),

Conv2D(256, 5, strides=1, activation="relu", name="conv2_1",
kernel_initializer="glorot_uniform", data_format="channels_last"),
BatchNormalization(axis=-1, momentum=0.9, epsilon=1e-6),
MaxPool2D(pool_size=(3,3), strides=(2,2)),
Dropout(rate=0.3),

Conv2D(384, 3, strides=1, activation="relu", name="conv3_1",
kernel_initializer="glorot_uniform", data_format="channels_last"),

Conv2D(256, 3, strides=1, activation="relu", name="conv3_2",
kernel_initializer="glorot_uniform", data_format="channels_last"),
MaxPool2D(pool_size=(3,3), strides=(2,2)),
Dropout(rate=0.3),

Flatten(name="flatten"),
Dense(1024, activation="relu", kernel_regularizer=L2(l2=5e-4),
kernel_initializer='glorot_uniform'),
Dropout(rate=0.5),

Dense(128, activation="relu", kernel_regularizer=L2(l2=5e-4),
kernel_initializer='glorot_uniform'),
], name="base_network")
return seq

def sq_euclid_dist(pair):
"""Squared Euclidean distance between two vectors"""
a, b = pair
return K.sum(K.square(a - b), axis=-1)

def contrastive(y_true, y_pred):

If similar (y_true==0), prefer zero Euclidean distance
If dissimilar (y_true==1), prefer Euclidean distance at max
'''
maxdist = 1.0
similar = tf.cast(1-y_true, tf.float32) * y_pred
dissimilar = tf.cast(y_true, tf.float32) * tf.maximum(maxdist - y_pred, 0)
return tf.reduce_mean(similar + dissimilar, axis=-1)

# Build Siamese network
base_network = make_base_signet()

input_a = Input(shape=IMAGE_SHAPE, name="input_a")
input_b = Input(shape=IMAGE_SHAPE, name="input_b")
output_a = base_network(input_a)
output_b = base_network(input_b)
distance = Lambda(sq_euclid_dist, name="dist")([output_a, output_b])

model = Model(inputs=[input_a, input_b], outputs=distance)
model.compile(loss=contrastive,
optimizer=tf.keras.optimizers.RMSprop(learning_rate=1e-4, rho=0.9, epsilon=1e-8))


The code above is not difficult to train. And surprisingly quite easy to get close to perfect prediction capability with the dataset. While the paper mentioned about the preprocessing of the image such as normalizing the pixel values, it seems unnecessary due to the batch normalization layer after the first convolutional layer.

In case of training, the paper mentioned the case of imbalanced data: There are 55 signer in the CEDAR dataset and 24 each for genuine and forgery. So the total pairs of genuine match per signer would be 24C2 = 276. But for forgery-genuine pair, there would be 24x24=576. If we count the case of unskilled forged signatures, i.e., using the genuine signature of a different signer to pair up as a forged pair, there would be even more. The paper suggested to use subsampling to handle this.

The output of the siamese network is the distance. We need to find a threshold for genuine/forge decision. This can be done by picking a point from the ROC curve.

## Bibliographic data

@unpublished{
title = "SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification",
author = "Sounak Dey and Anjan Dutta and J. Ignacio Toledo and Suman K.Ghosh and Josep Lladós and Umapada Pal",
year = "2017",
arxiv = "1707.02131.pdf",
}