Xiang Li 교수

Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e.g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations due to the covariate overwhelm those due to the identity. We therefore propose a method of gait recognition via disentangled representation learning that considers both identity and covariate features

Clothing and carrying status variations are the two key factors that affect the performance of gait recognition because people usually wear various clothes and carry all kinds of objects, while walking in their daily life. These covariates substantially affect the intensities within conventional gait representations such as gait energy images. Hence, to properly compare a pair of input gait features, an appropriate metric for joint intensity is needed in addition to the conventional spatial metr

We propose an end-to-end model-based cross-view gait recognition which employs pose sequences and shapes extracted by human model fitting. Specifically, we consider a problem setting where gait sequences from single different views are given as a pair to match in a test phase, while asynchronous multi-view gait sequences are given for each subject in a training phase. This work exploits multi-view constraint in the training phase to extract more consistent pose sequences from any views in the te

Human age estimation from gait is expected to be an important technology for a variety of applications such as automatic customer counting for marketing research or automatic age-based access control restriction for a specific area because the gait can be observable at a distance from a camera (e.g., CCTV). Although the aging process of gait significantly differs among age groups (e.g., children, adults, and the elderly), previous studies on gait-based human age estimation employ a single age gr

Existing model-based gait databases provide the 2D poses (i.e., joint locations) extracted by general pose estimators as the human model. However, these 2D poses suffer from information loss and are of relatively low quality. In this paper, we consider a more informative 3D human mesh model with parametric pose and shape features, and propose a multi-view training framework for accurate mesh estimation. Unlike existing methods, which estimate a mesh from a single view and suffer from the ill-pos

Gait recognition invariant to carried objects (COs) is very difficult in a real-life scene because the COs can have various shapes and sizes, in addition to unpredictable carrying locations (e.g., front, back, and side, or multiple locations). Therefore, in this paper, we propose a robust method for gait recognition against various COs by reconstructing a gait template without COs. A straightforward approach is to directly generate a gait template without COs given a gait template with COs as th

Existing approaches to gait-based human age estimation seldom consider variations such as carried objects, which greatly alter appearance of gait features (e.g., gait energy images) and result in poor age estimation results. Therefore, we propose a method of gait-based human age estimation robust against carrying status using generative adversarial networks. Specifically, we consider a generative network that outputs a gait feature without carried objects (i.e., it makes the carried objects disa

Silhouette-based gait representations are widely used in the current gait recognition community due to their effectiveness and efficiency, but they are subject to changes in covariate conditions such as clothing and carrying status. Therefore, we propose a gait energy response function (GERF) that transforms a gait energy (i.e., an intensity value) of a silhouette-based gait feature into a value more suitable for handling these covariate conditions. Additionally, since the discrimination capabil

Online social networks (OSNs) are known to be vulnerable to Sybil Attack, where attackers leverage the openness to create multiple fake identities for launching many malicious activities. In this paper, we define a weighted-strong-social (WSS) graph that integrates the OSN structure and user behavior patterns and propose a novel hybrid graph-based sybil detection approach. The hybrid approach estimates the trustworthiness of users and user pairs based on user behaviors that can be obtained local

Identifying brain-tissue types holds significant research value in the biomedical field of non-contact brain-tissue measurement applications. In this paper, a layered metastructure is proposed, and the second harmonic generation (SHG) in a multilayer metastructure is derived using the transfer matrix method. With the SHG conversion efficiency (CE) as the measurement signal, the refractive index ranges that can be distinguished are 1.23~1.31 refractive index unit (RIU) and 1.38~1.44 RIU, with sen

The Directionlet is an anisotropic multi-direction method with perfect reconstruction and critical sampling based on lattice, and it has obvious advantages in image edges. Based on analysing different EPMA image features, this paper starts extracting edges based on integral lattice, setting corresbonding windows to compare mean value and standard deviation, and then begin weighted fusion based on window features. The experiment shows that this method can better describe the edge properties, as w

Gait-based human age and gender estimation has potential applications in visual surveillance, such as searching for specific pedestrian groups and automatically counting customers by different ages/genders. Unlike most existing methods that exploit widely used appearance-based gait features (e.g., gait energy image and silhouettes) or simple model-based gait features (e.g., leg length, stride width/frequency, and head-to-body ratio), we explore a recently popular 3D human mesh model (i.e., skinn

Aimed at the characteristics of the sample secondary electron and backscattered electron image as well as current image, this paper proposes a new image fusion algorithm based on second generation bandelet transform. Firstly, the bandelet transform can take advantage of the geometrical regularity of image structure, so we composite the images by bandelet transform, combining with the variety characteristics of micro-area image that the electron microprobe has acquired. Then, according to the cha