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.

CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

Abstract: Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0 .

Handbook of Remote Biometrics: for Surveillance and Security

Abstract: This comprehensive and innovative handbook covers aspects of biometrics from the perspective of recognizing individuals at a distance, in motion, and under a surveillance scenario. Features: Starts with a thorough introductory chapter; Provides topics from a range of different perspectives offered by an international collection of leading researchers in the field; Contains selected expanded contributions from the 5th IAPR International Summer School for Advanced Studies on Biometrics for Secure Authentication; Investigates issues of iris recognition, gait recognition, and touchless fingerprint recognition, as well as various aspects of face recognition; Discusses multibiometric systems, and machine learning techniques; Examines biometrics ethics and policy; Presents international standards in biometrics, including those under preparation. This state-of-the-art volume is designed to help form and inform professionals, young researchers, and graduate students in advanced biometric technologies.

Person-Specific Face Antispoofing With Subject Domain Adaptation

Abstract: Face antispoofing is important to practical face recognition systems. In previous works, a generic antispoofing classifier is trained to detect spoofing attacks on all subjects. However, due to the individual differences among subjects, the generic classifier cannot generalize well to all subjects. In this paper, we propose a person-specific face antispoofing approach. It recognizes spoofing attacks using a classifier specifically trained for each subject, which dismisses the interferences among subjects. Moreover, considering the scarce or void fake samples for training, we propose a subject domain adaptation method to synthesize virtual features, which makes it tractable to train well-performed individual face antispoofing classifiers. The extensive experiments on two challenging data sets: 1) CASIA and 2) REPLAY-ATTACK demonstrate the prospect of the proposed approach.

Advances in Biometric Person Authentication

Abstract: Face.- Texture Features in Facial Image Analysis.- Enhance ASMs Based on AdaBoost-Based Salient Landmarks Localization and Confidence-Constraint Shape Modeling.- Face Authentication Using One-Class Support Vector Machines.- A Novel Illumination Normalization Method for Face Recognition.- Using Score Normalization to Solve the Score Variation Problem in Face Authentication.- Gabor Feature Selection for Face Recognition Using Improved AdaBoost Learning.- An Automatic Method of Building 3D Morphable Face Model.- Procrustes Analysis and Moore-Penrose Inverse Based Classifiers for Face Recognition.- Two Factor Face Authentication Scheme with Cancelable Feature.- Fingerprint.- Local Feature Extraction in Fingerprints by Complex Filtering.- A TSVM-Based Minutiae Matching Approach for Fingerprint Verification.- A Robust Orientation Estimation Algorithm for Low Quality Fingerprints.- An Exact Ridge Matching Algorithm for Fingerprint Verification.- Adaptive Fingerprint Enhancement by Combination of Quality Factor and Quantitative Filters.- Fingerprint Classification Based on Statistical Features and Singular Point Information.- Iris.- An Iterative Algorithm for Fast Iris Detection.- A Non-linear Normalization Model for Iris Recognition.- A New Feature Extraction Method Using the ICA Filters for Iris Recognition System.- Iris Recognition Against Counterfeit Attack Using Gradient Based Fusion of Multi-spectral Images.- An Iris Detection Method Based on Structure Information.- Speaker.- Constructing the Discriminative Kernels Using GMM for Text-Independent Speaker Identification.- Individual Dimension Gaussian Mixture Model for Speaker Identification.- Writing.- Sensor Interoperability and Fusion in Signature Verification: A Case Study Using Tablet PC.- Fusion of Local and Regional Approaches for On-Line Signature Verification.- Text-Independent Writer Identification Based on Fusion of Dynamic and Static Features.- Gait.- Combining Wavelet Velocity Moments and Reflective Symmetry for Gait Recognition.- Model-Based Approaches for Predicting Gait Changes over Time.- Other Biometrics.- Using Ear Biometrics for Personal Recognition.- Biometric Identification System Based on Dental Features.- A Secure Multimodal Biometric Verification Scheme.- Automatic Configuration for a Biometrics-Based Physical Access Control System.

Skin Detection -a Short Tutorial

Abstract: Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approach es have been developed for skin detection. A skin detector typically transforms a given pix el into an appropriate color space and then use a skin classifier to label the pixel whether it is a ski n or a non-skin pixel. A skin classifier defines a decision boundary of the skin color class in the colo r space based on a training database of skin-colored pixels.

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.