Stan Z. Li

Bio: Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Face detection. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.

3DPC-Net: 3D Point Cloud Network for Face Anti-spoofing

Abstract: Face anti-spoofing plays a vital role in face recognition systems. Most deep learning-based methods directly use 2D images assisted with temporal information (i.e., motion, rPPG) or pseudo-3D information (i.e., Depth). The main drawback of the mentioned methods is that another extra network is needed to generate the depth/rPPG information to assist the backbone network for face anti-spoofing. Different from these methods, we propose a novel method named 3D Point Cloud Network (3DPC-Net). It is an encoder-decoder network that can predict the 3DPC maps to discriminate live faces from spoofing ones. The main traits of the proposed method are that: 1) It is the first time that 3DPC is used for face anti-spoofing; 2) 3DPC-Net is simple and effective and it only relies on 3DPC supervision. Extensive experiments on four databases (i.e., Oulu-NPU, SiW, CASIA-FASD, Replay Attack) have demonstrated that the 3DPC-Net is comparative to the state-of-the-art methods.

Comparisons of grain refinement and recalescence behavior during the rapid solidification of undercooled Cu–Co and Cu–Ni alloys

Abstract: Applying the melt-fluxing method, rapid solidifications of undercooled Cu 50 Co 50 immiscible and Cu 70 Ni 30 solid solution alloys were performed to investigate the grain refinement regularity at moderate undercooling. Due to the essential distinction of physical properties between these alloys, grain refinement phenomenon was detected in Cu 50 Co 50 alloy with a tendency of liquid separation, while dendrite structure appears for Cu 70 Ni 30 alloy. The association of microstructure morphology to non-equilibrium solidification process was constructed with the description of recalescence degree as a function of undercooling. Quantitative thermodynamic calculation for the undercooled liquid of the researched systems was carried out to elucidate the influence of liquid separation on the variation of Gibbs free energy for the subsequent rapid solidification, which gives a better understanding of the as-observed experimental results.

Content-based retrieval of video shot using the-improved nearest feature line method

Abstract: Shot-based classification and retrieval is very important for video database organization and access. We present a new approach: 'nearest feature line - NFL' used in shot retrieval. We look at key-frames in a shot as feature points to represent the shot in feature space. Lines connecting the feature points are further used to approximate the variations in the whole shot. The similarity between the query image and the shots in video database are measured by calculating the distance between the query image and the feature lines in feature space. To make it more suited to video data, we improved the original NFL method by adding constraints on the feature lines. Experimental results show that our improved NFL method is better than the traditional classification methods such as nearest neighbor (NN) and nearest center (NC).

Learning discriminant face descriptor for face recognition

Abstract: Face descriptor is a critical issue for face recognition. Many local face descriptors like Gabor, LBP have exhibited good discriminative ability for face recognition. However, most existing face descriptors are designed in a handcrafted way and the extracted features may not be optimal for face representation and recognition. In this paper, we propose a learning based mechanism to learn the discriminant face descriptor (DFD) optimal for face recognition in a data-driven way. In particular, the discriminant image filters and the optimal weight assignments of neighboring pixels are learned simultaneously to enhance the discriminative ability of the descriptor. In this way, more useful information is extracted and the face recognition performance is improved. Extensive experiments on FERET, CAS-PEAL-R1 and LFW face databases validate the effectiveness and good generalizations of the proposed method.

Modeling Bayesian estimation for deformable contours

Abstract: A novel trainable snake model called EigenSnake, is presented in the Bayesian framework. In the EigenSnake, prior knowledge of a specific object shape, such as that of face outlines and facial features, is derived from a training set of the shape and incorporated into a Bayesian snake model in the form of the prior distribution. Further, a "shape space", which is constructed on the basis of a set of eigenvectors obtained from principle component analysis, is used to restrict and stabilize the search for the optimal solution. The effectiveness is demonstrated by experiments, which shows that the EigenSnake produces more reliable and accurate results than existing models.

You Only Look Once: Unified, Real-Time Object Detection

Abstract: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Robust Face Recognition via Sparse Representation

Abstract: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.