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Accurate face recognition technology is of great significance for face anti-counterfeiting. Due to illumination, posture, angle, and other reasons, the existing face liveness detection technology is difficult to adapt the environmental changes, resulting in low detection accuracy. To address this issue, this paper presents a novel high-performance face anti-spoofing detection method named RGCS_ConvNeXt. The data-enhanced face images are fed into the ConvNext network, which group convolution is added to extract the correlation between different features, and the coordinate attention mechanism is used to enhance the facial feature extraction capability both spatially and directionally. Then SPPF is used to extract the features at different scales to enhance the representation of the feature map. Finally, the facial key point detection technique is utilized to calculate the eye EAR value to achieve accurate face anti-counterfeiting recognition. The proposed algorithm shows an average classification error rate of 0.3%, 1.7%, 1.9%±1.5% and 2.8%±3.4%, respectively, on the four protocols of the OULU-NPU public dataset. On the Siw dataset, the average classification error rate is 0.69%, a reduction of 0.02% compared to the MA-Net network. The half-error rate on the MSU-MFSD dataset is 2.39%, a 0.21% reduction compared to the DPCNN network. The algorithm shows good accuracy on the OULU-NPU, MSU-MFSD and Siw datasets, reaching 99.64%, 98.40% and 99.25% respectively, 0.26% higher than the SE-FeatherNet network’s average accuracy.

The CA-YOLO (Coordinate Attention-YOLO) model has been optimized for object detection in complex remote sensing images, addressing key issues faced by algorithms that detect multiple objects. These issues include weak multi-scale feature learning capabilities and the challenging trade-off between detection accuracy and model parameter complexity. The CA-YOLO model, built on the framework of YOLOv5, incorporates a lightweight coordinate attention module in the shallow layer to improve detailed feature extraction and suppress redundant information interference. Additionally, a spatial pyramid pooling-fast with a tandem construction module is implemented in the deeper layer. The model employs a stochastic pooling strategy to fuse multi-scale key feature information from low-level to high-level layers, reducing the number of model parameters while improving inference speed. We optimized the anchor box mechanism and modified loss function to improve the ability of the model to detect objects of different sizes and scales. Results show that the CA-YOLO model outperforms the original YOLO in terms of multi-object detection accuracy, with an average mAP@0.5 accuracy improvement of 4.8% and mAP@0.5:0.95 accuracy improvement of 3.8%. Additionally, the CA-YOLO model demonstrates exceptional inference speed, averaging 125 fps, which reinforces its superiority in detection accuracy, generalization ability, and overall efficiency. Notably, these improvements were achieved while maintaining the same number of parameters and complexity as other models, making the CA-YOLO model an exceptional choice for various applications.