Pattern Recognition

Most of current person re-identification works identify a person by matching his/her probe image against a galley images set. One feasible way to improve the identification accuracy is the multi-shot re-identification, where the probe includes a small ...

In this paper, a method to improve texture analysis performance in biometrics by adjusting image sharpness is presented. Images of high sharpness are usually considered as high quality data in texture analysis. Therefore, the imaging sensor and lens are ...

Finger vein recognition has drawn increasing attention from biometrics community due to its security and convenience. In this paper, a novel discriminative binary codes (DBC) learning method is proposed for finger vein recognition. First of all, subject ...

Extracting reliable finger-vein features directly from original finger-vein images is not an easy task since the captured finger-vein images are always poor in quality. This paper proposes an effective method of finger-vein feature representation based ...

Due to the short duration and low intensity of micro-expressions, the recognition of micro-expression is still a challenging problem. In this paper, we develop a novel multi-task mid-level feature learning method to enhance the discrimination ability of ...

Recently, tremendous improvements have been achieved for facial landmark localization on static images. However, detecting and tracking facial shapes in sequential images is still challenging due to the large appearance variations in unconstrained ...

As performance on some aspects of the Labeled Faces in the Wild (LFW) benchmark approaches 100% accuracy, there is an intense debate on whether unconstrained face verification problem has already been solved. In this paper, we study a new face ...

A number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features. We show that visual regression can be substantially improved by two-stage ...

In this paper, we propose a group-aware deep feature learning (GA-DFL) approach for facial age estimation. Unlike most existing methods which utilize hand-crafted descriptors for face representation, our GA-DFL method learns a discriminative feature ...

Age estimation from face images is an important yet difficult task in computer vision. Its main difficulty lies in how to design aging features that remain discriminative in spite of large facial appearance variations. Meanwhile, due to the difficulty ...

Given a face image, the problem of age estimation is to predict the actual age from the visual appearance of the face. In this work, we investigate this problem by means of the deep learning techniques. We comprehensively diagnose the training and ...

With the aid of a universal facial variation dictionary, sparse representation based classifier (SRC) has been naturally extended for face recognition (FR) with single sample per person (SSPP) and achieved promising performance. However, extracting ...

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L1 or L2 ...

Face verification, which aims to determine whether two face images belong to the same identity, is an important task in multimedia area. Face verification becomes more challenging when the person is wearing makeup. However, collecting sufficient makeup ...

The hue and chroma components of an image pixel carry crucial information that can be exploited to perform segmentation. However, due to its directional property, a circular distribution is required to characterize the hue component. In this article, we ...

Defect detection and classification of ceramic tile surface defects occurred in firing units are usually performed by human observations in most factories. In this paper, an automatic image processing system with high accuracy and time efficient ...

In this work, we propose a novel method for rectifying damaged motion sequences in an unsupervised manner. In order to achieve maximal accuracy, the proposed model takes advantage of three key properties of the data: their sequential nature, the ...

Devising a representation suitable for characterizing human actions on the basis of a sequence of pose estimates generated by an RGBD sensor remains a research challenge. We here provide two insights into this challenge. First, we show that discriminate ...

This paper mainly introduces a novel deep learning and mapping (DLM) framework oriented to the ternary change detection task for information unbalanced images. Different from the traditional intensity-based methods available, the DLM framework is based ...

This paper presents a Grammar-aware Driver Parsing (GDP) algorithm, with deep features, to provide a novel driver behavior situational awareness system (DB-SAW). A deep model is first trained to extract highly discriminative features of the driver. Then,...

In this paper, we propose a generic framework for globally consistent alignment of images captured from approximately planar scenes via topology analysis, capable of resisting the perspective distortion meanwhile preserving the local alignment accuracy. ...

Over the past decade, salient object detection has attracted a lot of interests in computer vision. Although many models have been proposed to detect the salient object in an arbitrary image, this problem is still plagued with complex backgrounds and ...

In this paper, we propose an efficient method for salient region detection. First, the image is decomposed by using superpixel segmentation which groups similar pixels and generates compact regions. Based upon the generated superpixels, similarity ...

Subspace learning methods have played an important role on handling the high-dimensional gait template for human identification. Particularly, linear discriminant analysis (LDA) method has been widely applied to find one discriminant low-dimensional ...

Cancelable fingerprint templates effectively protect original fingerprint data by revoking a compromised template and reissuing a new one. Alignment-free cancelable templates require no image pre-alignment and therefore do not suffer from inaccurate ...

Illumination problem is still a bottleneck of robust face recognition system, which demands extracting illumination invariant features. In this field, existing works only consider the variations caused by lighting direction or magnitude (denoted as ...

Multi-label feature selection involves the selection of relevant features from multi-labeled datasets, resulting in a potential improvement of multi-label learning accuracy. In conventional multi-label feature selection methods, the final feature subset ...

Several dimensionality reduction methods based on the max-min idea have been proposed in recent years and can obtain good classification performance. In this paper, inspired by the idea, we develop max-min linear discriminant analysis (MMLDA), which ...

Unsupervised feature selection (UFS) aims to reduce the time complexity and storage burden, improve the generalization ability of learning machines by removing the redundant, irrelevant and noisy features. Due to the lack of training labels, most ...