Pengfei Shi

Bio: Pengfei Shi is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 21, co-authored 74 publications receiving 1612 citations.

An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

Abstract: An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.

Handwritten Bangla numeral recognition system and its application to postal automation

Abstract: A recognition system for handwritten Bangla numerals and its application to automatic letter sorting machine for Bangladesh Post is presented. The system consists of preprocessing, feature extraction, recognition and integration. Based on the theories of principal component analysis (PCA), two novel approaches are proposed for recognizing handwritten Bangla numerals. One is the image reconstruction recognition approach, and the other is the direction feature extraction approach combined with PCA and SVM. By examining the handwritten Bangla numeral data captured from real Bangladesh letters, the experimental results show that our proposed approaches are effective. To meet performance requirements of automatic letter sorting machine, we integrate the results of the two proposed approaches with one conventional PCA approach. It has been found that the recognition result achieved by the integrated system is more reliable than that by one method alone. The average recognition rate, error rate and reliability achieved by the integrated system are 95.05%, 0.93% and 99.03%, respectively. Experiments demonstrate that the integrated system also meets speed requirement.

Facial expression recognition on multiple manifolds

Abstract: Manifold learning has been successfully applied to facial expression recognition by modeling different expressions as a smooth manifold embedded in a high dimensional space. However, the assumption of single manifold is still arguable and therefore does not necessarily guarantee the best classification accuracy. In this paper, a generalized framework for modeling and recognizing facial expressions on multiple manifolds is presented which assumes that different expressions may reside on different manifolds of possibly different dimensionalities. The intrinsic features of each expression are firstly learned separately and the genetic algorithm (GA) is then employed to obtain the nearly optimal dimensionality of each expression manifold from the classification viewpoint. Classification is performed under a newly defined criterion that is based on the minimum reconstruction error on manifolds. Extensive experiments on both the Cohn-Kanade and Feedtum databases show the effectiveness of the proposed multiple manifold based approach.

Statistical texture analysis-based approach for fake iris detection using support vector machines

Abstract: This paper presents a novel statistical texture analysis based method for detecting fake iris. Four distinctive features based on gray level co-occurrence matrices (GLCM) and properties of statistical intensity values of image pixels are used. A support vector machine (SVM) is selected to characterize the distribution boundary, for it has good classification performance in high dimensional space. The proposed approach is privacy friendly and does not require additional hardware. The experimental results indicate the new approach to be a very promising technique for making iris recognition systems more robust against fake-iris-based spoofing attempts.

Object Tracking using Incremental 2D-PCA Learning and ML Estimation

Abstract: Video surveillance has drawn increasing interests in recent years. This paper addresses the issue of moving object tracking from videos. A two-step processing procedure is proposed: an incremental 2DPCA (two-dimensional principal component analysis)-based method for characterizing objects given the tracked regions, and a ML (maximum likelihood) blob-tracking process given the object characterization and the previous blob sequence. The proposed incremental 2DPCA updates the row- and column-projected covariance matrices recursively, and is computationally more efficient for online learning of dynamic objects. The proposed ML blob-tracking takes into account both the shape information and object characteristics. Tests and evaluations were performed on indoor and outdoor image sequences containing a range of single moving object in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.

Image understanding for iris biometrics: A survey

Abstract: This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.

Age Synthesis and Estimation via Faces: A Survey

Abstract: Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.

Traditional and recent approaches in background modeling for foreground detection: An overview

Abstract: Background modeling for foreground detection is often used in different applications to model the background and then detect the moving objects in the scene like in video surveillance. The last decade witnessed very significant publications in this field. Furthermore, several surveys can be found in literature but none of them addresses an overall review in this field. So, the purpose of this paper is to provide a complete survey of the traditional and recent approaches. First, we categorize the different approaches found in literature. We have classified them in terms of the mathematical models used and we have discussed them in terms of the critical situations that they claim to handle. Furthermore, we present the available resources, datasets and libraries. Then, we conclude with several promising directions for future research.

A survey of appearance models in visual object tracking

Abstract: Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.

A Survey of Appearance Models in Visual Object Tracking

Abstract: Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are four-fold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source code and video datasets) are examined in this survey.