OPTransformer: An Overlapped Patch Vision Transformer for Barefoot Footprint Identification

In recent years, barefoot footprint recognition has become increasingly important in criminal investigations. However, the high complexity and diverse features of footprint images pose challenges to traditional methods and CNNs due to their limited local receptive fields. To address this problem, we propose OPTransformer, an improved Vision Transformer (ViT) that integrates global and local features for robust barefoot footprint recognition. Specifically, an overlapped patch strategy is introduced to enhance inter-patch information exchange, a depthwise separable convolution module is embedded to strengthen fine-grained local feature extraction with low computational cost, and an efficient channel attention (ECA) mechanism is used to recalibrate feature channels. Experiments on the SXU-FOOT dataset under a closed-set protocol show that OPTransformer achieves a Top-1 accuracy of 95.33%, outperforming ViT, ResNet, Swin Transformer, and EfficientNetV2. The reported ablations support the contribution of individual modules but do not isolate their pairwise interactions. This work provides an approach to closed-set barefoot footprint classification and can be extended in the future with multimodal fusion and lightweight model design.