Gait recognition has become an increasingly important biometric technique for identifying individuals from a distance without requiring their active cooperation. Since gait involves a sequence of motion patterns, effectively capturing temporal dynamics is essential for accurate recognition. Traditional methods that extract temporal features independently and fuse them at a later stage often fail to model the continuity and interdependence of motion across frames. To overcome this limitation, we propose a novel three-dimensional convolutional architecture named Robust Spatiotemporal 3D Convolutional Neural Network (RST3D), which jointly captures spatial and temporal correlations throughout gait sequences. The proposed architecture incorporates a comprehensive 3D convolutional block that operates along the temporal, height, and width dimensions, enabling the network to learn more expressive and coherent spatiotemporal representations. In addition, we introduce a Temporal Pyramidal Attention (TPA) block to enhance the network’s ability to model temporal dependencies by capturing discriminative motion patterns across multiple temporal scales. We evaluate our method on four large-scale gait recognition datasets: CASIA-B, OUMVLP, GREW, and Gait3D. Experimental results show that our approach consistently achieves superior performance compared to existing 3D CNN-based methods, particularly under challenging conditions such as view variation, clothing changes, and occlusion.

Existing pose-invariant face recognition mainly focuses on frontal or profile, whereas high-pitch angle face recognition, prevalent under surveillance videos, has yet to be investigated. More importantly, tilted faces significantly differ from frontal or profile faces in the potential feature space due to self-occlusion, thus seriously affecting key feature extraction for face recognition. In this paper, we asymptotically reshape challenging high-pitch angle faces into a series of small-angle approximate frontal faces and exploit a statistical approach to learn texture features to ensure accurate facial component generation. In particular, we design a statistical texture-guided GAN for tilted face frontalization (STG-GAN) consisting of three main components. First, the face encoder extracts shallow features, followed by the face statistical texture modeling module that learns multi-scale face texture features based on the statistical distributions of the shallow features. Then, the face decoder performs feature deformation guided by the face statistical texture features while highlighting the pose-invariant face discriminative information. With the addition of multi-scale content loss, identity loss and adversarial loss, we further develop a pose contrastive loss of potential spatial features to constrain pose consistency and make its face frontalization process more reliable. On this basis, we propose a divide-and-conquer strategy, using STG-GAN to progressively synthesize faces with small pitch angles in multiple stages to achieve frontalization gradually. A unified end-to-end training across multiple stages facilitates the generation of numerous intermediate results to achieve a reasonable approximation of the ground truth. Extensive qualitative and quantitative experiments on multiple-face datasets demonstrate the superiority of our approach.

Recently, face super-resolution has achieved pleasing performance. Numerous works have shown that texture features and structural information play a crucial role for super-resolution reconstruction. However, effective co-learning of both has been limiting the performance improvement of existing state-of-the-art methods. Therefore, we focus on the texture and structure of images in this paper, and design a two-branch network containing a texture network (T-Net) and a structure network (S-Net) to jointly explore texture and structure information for co-learning. T-Net serves as the backbone network to learn both texture and structure information for reconstruction, while the S-Net serves as the auxiliary network that can effectively exploit the multi-scale information of the T-Net encoder to recover the structure. To better facilitate the co-learning of the two branches, two co-learning modules deal with the information flow interaction between the encoder and decoder of the two branches, respectively, thus explicitly guiding the structure-aware image reconstruction. Additionally, a dense feature enhancement module investigates the channel and spatial correlation of features and enhances the representation capability of the network. Extensive experiments on the CelebA and Helen datasets show that our proposed approach outperforms state-of-the-art methods.

Video face recognition benefits profoundly from deep convolutional neural networks (CNNs), which learn robust feature embeddings. However, due to their fixed geometric structures, CNNs are inherently limited in modeling the significant variations from the angle, pose, occlusion and other factors of face images. In this paper, a neural aggregation network based on mutual relation learning is proposed for video face recognition. First, Intra-frame Relational Learning network (Intra-Net) is introduced, which models the interdependencies between the re-gional components of individual features and develops relevance between fine-grained features. Such processing can determine the region of interest adaptively according to the quality of the input face image to achieve the extraction of valuable information. Secondly, we introduce Inter-frame Relational Learning Network (Inter-Net), which considers the most significant appearance representation in the overall structure of the face image to cor-relate the complementarity of features between frames. Finally, information aggregation is performed by combining Inter-Net and Intra-Net. Joint optimization of the two branches allows our model to effectively exploit the complementary information between them to improve the aggregation capability. We validate the effectiveness of our model for video face recognition, proving its superiority over state-of-the-art methods on two benchmark datasets.

Video action recognition task involves modeling spatiotemporal information, and efficiency is critical to capture spatiotemporal dependencies in the video. Most existing models rely on optical flow information to capture the dynamic visual tempos between consecutive video frames. Although impressive performance can be achieved by combining optical flow with RGB, the time-consuming nature of optical flow computation cannot be ignored. Moreover, 3D CNN has successfully modeled spatiotemporal information, yet the enormous computational volume is unsuitable for real-time action recognition. In this letter, we propose a novel lightweight video feature extraction strategy that achieves better recognition performance with lower FLOPs. In particular, we perform convolution on the video cube from three orthogonal angles to learn its appearance and motion features. Compared with the computational volume of 3D CNN, our proposed method is more economical and thus meets the lightweight requirements. Extensive experimental results on public Something Something-V1$\&$V2 and Diving48 datasets show our approach achieves the state-of-the-art performance.

Compared to face recognition, gait recognition is one of the most promising video biometric recognition technologies given that gait images can be readily captured at a distance and gait characteristics are robust to appearance camouflage. A lot of existing gait recognition methods aim at a single scene such as fixed cameras, but the recognition accuracy will decrease sharply if the viewpoints are changed. In this paper, we improve the existing methods and propose a cross-view gait recognition method based on feature fusion. Firstly, a multi-scale feature fusion module is proposed to extract the features of gait sequences with different granularities. Then, a dual-path structure is introduced to learn global appearance features and fine-grained local features, respectively. The features of two paths are gradually merged as the network deepens to obtain the complementary information. In the last feature mapping stage, the Generalized-Mean pooling is used to favour discriminative representation. Extensive experiments on the public dataset CASIA-B show that our method can achieve state-of-the-art recognition performance.