Age prediction of barefoot images based on ResNet and dual-attention mechanism
Footprints are the traces left when a person's foot comes into contact with an object such as the ground while walking,and they are also one of the most common biological features be left be-hind by a suspect at a crime scene,Footprints can provide information about a person's,weight,gender,age and other identity attributes,Using footprint information for human age prediction,to indicate the direction of reconnaissance and narrow the scope of reconnaissance,is of great signifi-cance.In traditional investigations,forensic experts make predictions about the identity and attributes of suspects based on accumulated case experience and footprints left at the scene,but this process requires a great deal of domain knowledge.Accordingly,an automatic age prediction method based on barefoot images is proposed in this paper,which consists of a pseudo-color transformation module,an online random geometric transformation module,a feature extraction module,a spatial attention module and an age prediction module.The algorithm was tested on a dataset consisting of 1,818 barefoot greyscale images,and the prediction accuracy metrics of Acc_5 and Acc_10 reached 55.5％and 83.4％,respectively,which are better than existing age prediction methods.
age predictionbarefoot imagesResNet18 networkbottleneck attention mechanismsspa-tial attention mechanisms
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