Ziwei Huang, Ying Shu, Fanghao, Quanyu Long, Wenya Wang, Qiushi Guo, Tiezheng Ge, Leilei Gan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy. The security of facial recognition is also highly regarded. The Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of face recognition systems. Traditional FAS used images containing identity information to detect spoofing traces, however there is a risk of privacy leakage during the transmission and storage of these images. Besides, the encryption and decryption of these privacy-sensitive data takes too long compared to inference time by FAS model. To address the above issues, we propose a face anti-spoofing algorithm based on facial skin patches leveraging pure facial skin patch images as input, which contain no privacy information, no encryption or decryption is needed for these images. We conduct experiments on several public datasets, the results prove that our algorithm has demonstrated superiority in both accuracy and speed.

Fine-grained image classification has drawn increasing attention as it is much closer to practical applications than generic image classification. The majority of current fine-grained approaches locate the discriminative regions and leverage the features of these regions for classification as their magic weapons. However, these approaches simply ignore the internal semantic region correlation. As is well known, the correlation reveals the salient information of images, which can further boost the performance of fine-grained image classification. To this end, we propose an Object Decoupling with Graph Correlation network (ODGC) to explore the informative potentials of region correlation. A Responsive Object Location Module (ROLM) is first introduced to obtain the fine-grained object within a bounding box automatically. A Semantic Decoupling Module (SDM) then segments the object into different parts. ODGC learns the representations of these parts by transferring these part features into a Graph Correlation Module (GCM). Consists of these three main modules, ODGC is trained for fine-grained image classification in an end-to-end way. Extensive experiments conducted on CUB-200-2011 demonstrate that the aforementioned modules significantly improve the ODGC, and it achieves a new state-of-the-art performance to 88.2% top-1 accuracy. Besides, we collect a practical business e-commercial dataset, named Ecom-15K. The evaluation on it further validates the applicability of our method in practical scenarios.

In this paper, we describe a multi-scale machine learning approach to estimate socio-economic attributes of citizens based on the analysis of aerial images. To analyse the effectiveness of the proposed approach we predict building property value classes. The classification of these building property values is a proxy for the socio-economic status of the residents. The approach is based on the fusion of deep Convolutional Neural Networks (CNNs). We compare the proposed approach with non-image and single-scale CNN approaches and demonstrate the effectiveness in a case study using statistical data collected in the city of Amsterdam, Netherlands. We show that the proposed multi-scale approach outperforms the baseline methods.