Research On Barefoot Footprint Classification Based On Few-Shot Learning

The footprint is the trace that has a high legacy rate on the scene,which not only has a pivotal position in the field of criminal investigation,but also has a wide range of applications in the production of shoes,medical and so on.However,for footprint classification,the traditional classification research often rely on the experienced footprint experts that extract the features of the physical significance of the footprint.Duo to their relevant experience is limited,and the method for feature extraction also is different,so that different researchers get the features that is difficult to define good or bad,in addition,some abstract feature relationship between different regions of the footprint is hard to be extracted.With the development of deep learning,convolutional neural network has achieved remarkable performance in many fields.Relevant footprint researchers have also applied the deep learning methods to solve the footprint classification,at the same time,there are some problems.On the one hand,it is difficult to obtain a large number of footprint samples from the same person,while it is often easier to obtain one or several footprint samples.On the other hand,the footprint is fine-grained image,and the difference between the footprints of different people is more subtle,so that it is generally difficult to distinguish the category.Aiming at shortcomings existing footprint research,this paper can learn a large number of task distributions in the task space based on the meta-learning,so as to improve the generalization ability of the model to new tasks with few samples.The main research contents are as follows:(1)The footprint data was collected and preprocessed according to collection specifications.From a static and dynamic perspective,this paper collected static 114 and dynamic 142 people's optical footprint data respectively based on the optical footprint acquisition instrument.In order to acquire more meta-knowledge,pressure footprint images of 63 people and some optical footprint images of people from relevant departments in Nanjing were used to expand the data set in this paper.In order to obtain high-quality footprint samples,the collected footprint samples are de-noised and standardized to reduce the difference in data distribution.(2)In order to overcome the shortcomings of relation network of few-shot learning,a footprint classification algorithm of channel-spatial attention is proposed.This method combines channel attention with spatial attention to mine the rich information of the samples.By embedding the joint attention mechanism in different convolutional layers,the focused feature map can be obtained from the multi-channel feature map,while some information of different regions can be obtained from the single feature map.Finally,the two kinds of information are fused effectively to improve the ability of feature expression ability.In this paper,a large number of experiments were carried out on common datasets,which showed the effectiveness of the method,and this algorithm was applied to the optical footprint classification,and the good results were obtained by training and testing the left and right feet separately.(3)Due to there are few optical footprint samples,and the channel-spatial attention model contains a large number of training parameters,so that the network parameters cannot get the optimal value.This paper proposes a bilinear network for footprint classification.For finegrained footprint,the bilinear network structure is used during feature extraction,this method can model between footprint image pixels in translation invariant,and obtain different regional important information.Finally,the similarity between different samples are computed using Euclidean.In particular,for several support samples,class prototype representation is obtained by computing mean of a few labeled samples belonging to a class,and the class prototype contains rich feature information.In this paper,a large number of experiments are carried out on the footprint data set,and the results show that this algorithm has better performance.