一种利用身体关键点实现精确步态识别的双向孪生循环神经网络

Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images

In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and c…

Model-based gait recognition using graph network on very large population database

At present, the existing gait recognition systems are focusing on developing methods to extract robust gait feature from silhouette images and they indeed achieved great success. However, gait can be sensitive to appearance features such as clothing and carried items. Compared with appearance-based method, model-based gait recognition is promising due to the robustness against these variations. In recent years, with the development of human pose estimation, the difficulty of model-based gait recognition methods has been mitigated. In this paper, to resist the increase of subjects and views var…

GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning

Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) a convolutional Autoencoder that transforms attributes of the biometric raw data, such as the gen…

几何足够吗？基于标记的注视估计评估

Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation

本文评估了基于面部标记的注视估计方法，通过标准化流程提取和归一化三个大型数据集的标记，并训练轻量级回归模型，发现其在跨域评估中与ResNet18基线相当，表明稀疏几何特征能有效支持鲁棒的注视估计。

Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos

In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision. We created a dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with (or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical sites located in 8 different states across the United States. We develop a computer vision pipeline to detect, track, and separate out t…

From Depth Data to Head Pose Estimation: a Siamese approach

The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss functio…

两阶段多视角步态识别与重嵌入网络

Two-Stage Multi-View Gait Recognition with a Re-Embedding Network

提出TFTR两阶段框架，通过孪生网络和Transformer重嵌入，实现多视角步态识别，在OU-MVLP和CASIA-B上取得高准确率。

用于生物特征分析的TinySiamese网络

TinySiamese Network for Biometric Analysis

本文提出TinySiamese网络，利用预训练CNN作特征提取器，以不超过7层的结构解决生物特征识别中标准Siamese网络计算量大、内存需求高的问题，在多数据集上显著降低训练与匹配时间并提升验证准确率。

RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training

Relative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmented reality and robotics. In this paper, we present an end-to-end relative camera pose estimation network using a siamese architecture that is independent of camera parameters. The network is trained using the Cambridge Landmarks data with four individual scene datasets and a dataset combining the four scenes. To improve generalization, we propose a novel two-stage training that alleviates the need of a hyperparamete…