Artificial Neural Network for Processing Fingerprint Image Noise

Abstract

Fingerprint images from crime scenes can be used to find and identify suspects in the field of forensic science. Fingerprint images are usually polluted by noise, which affects the visual effect of fingerprint images. It is important to process the noise of fingerprint images. A supervised learning 9-layer artificial neural network besides the input layer and the output layer is designed to remove the noise of fingerprint images in this paper. The first three layers of the neural network are convolutional layers. The purpose of designing convolutional layers is to extract the feature information of the images layer by layer and gradually generate the feature maps. The fourth layer to the sixth layer are fully connected layers. The number of neurons in the fourth layer is the same as that of the sixth layer. The number of neurons in the fourth layer is the number of elements in the vector flattened by the feature maps outputted from the last convolutional layer. The seventh layer to the ninth layer in the neural network are deconvolutional layers. These deconvolutional layers gradually restore the feature maps to the fingerprint image. Finally, the enhanced fingerprint image is outputted from the output layer. As the training samples of fingerprint images is inadequate, this paper provides a way to make training samples. The clear fingerprint image is blurred as the corresponding blurred fingerprint image, which solves the problem of lack of the training samples. The blurred fingerprint image and the corresponding clear fingerprint image are used to establish the training sample set and train the artificial neural network. The mean square error function is used as the loss function to adjust and optimize the parameters of the neural network. The neural network in this paper is evaluated on test fingerprint images. The neural network method is compared with the Gaussian low-pass filtering method and the Wiener filtering method. Experimental results show that the neural network method is superior to the Gaussian low-pass filtering method and the Wiener filtering method in filtering Gaussian noise of fingerprint images.