Crime Scene Shoeprint Classification Using Residual Network with Atrous Convolution

Abstract

Shoeprints have always been important evidences at crime scenes especially at burglary or theft In order to help the police automatically find the similar shoeprint in a large amount of shoeprint data and find the correlation between the cases this paper mainly proposes a convolutional neural network architecture for shoeprint classification Since the shoeprint images may have occlusion similar patterns and unclear edge conditions and make the classification task difficult we propose a residual network with atrous convolution which can make the field of view of filters larger and obtain more comprehensive feature information so the accuracy of classification is also improved In addition our proposed bounding box-based classification network performs better than the general image-based network because we divide the images into many bounding boxes to increase the training data The computational time at the training stage of bounding box-based network is also less than the training time of the patch-based network After completing the classification network we also study the incremental learning and apply it on our shoeprint classification model If the shoeprint database is updated in more classes how we make the classification model recognize new classes of shoeprints without retraining the network is an issue Because the number of shoeprint classes is unlimited we have to consider the maintenance costs The incremental learning algorithm can train the new network in the absence of the data of original classes and make the network can recognize the shoeprint images of both old classes and new classes

Date of Award	2019
Original language	English
Supervisor	Shu-Mei Guo (Supervisor)