基于身份感知模型的素描人脸识别方法

针对素描图像和光学图像之间存在较大的模态差异这一问题,提出了一种基于身份感知模型的素描人脸识别方法,实现跨模态图像生成和素描人脸识别。该方法应用新的感知损失来监督图像生成网络,生成更好的跨模态图像,减少模态差异带来的识别精度损失,并通过三元组损失来正则化类内和类间距离,增强识别模型的性能,用联合训练策略提升素描人脸识别能力。在UoM-SGFSv2、e-PRIP等素描人脸数据集上的实验结果表明,该方法识别效果优于其他对比算法。     

An efficient incremental face annotation for large scale web services

This paper proposes an incremental face annotation framework for sharing and publishing photographs which contain faces under a large scale web platform such as a social network service with millions of users. Unlike the conventional face recognition environment addressed by most existing works, the image databases being accessed by the large pool of users can be huge and frequently updated. A reasonable way to efficiently annotate such huge databases is to accommodate an adaptation of model parameters without the need to retrain the model all over again when new data arrives. In this work, we are particularly interested in the following issues related to increment of data: (i) the huge number of images being added at each instant, (ii) the large number of users joining the web each day, and (iii) the large number of classification systems being added at each period. We propose an efficient recursive estimation method to handle these data increment issues. Our experiments on several databases show that our proposed method achieves an almost constant execution time with comparable accuracy relative to those state-of-the-art incremental versions of principal component analysis, linear discriminant analysis and support vector machine.     

基于掌纹、人脸关联特征的身份识别算法

首先对掌纹、人脸图像进行融合;接着利用小波变换增强融合后图像;然后利用一种新的子空间分析方法--对角离散余弦变换和二维主元判别分析(Diagonal,Discrete Cosine Traasform and Two-Dimensional Principle Component Analysis,Dia-DCT+2DPCA)相结合的算法进行特征提取;最后运用最小距离分类器进行识别.实验结果表明,本文算法有效地提高了身份识别的正确识别率.     

Eigenface-domain super-resolution for face recognition

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.     

基于指纹采集的网络空间大规模侦测系统

随着信息化设备的更新换代,网络内部容易出现信息孤岛,造成IT资产管理混乱,进而影响信息系统的安全性和稳定性。基于指纹采集的网络空间大规模侦测系统通过采集网络空间的指纹信息,对网络设备组件进行识别,基于分布式架构设计并实现了大规模分布式侦测,极大提升了侦测速度和侦测效率。     

高维空间下求解MDA的扰动算法及其在人脸识别中的应用

本文提出了一种在高维空间下直接求MDA(Multiple discriminant analysis)最佳解的扰动算法，并把这一算法应用于人脸的识别中。在传统的MDA求解算法中，一般要求训练样本的个数足够大，以至于其类内散射矩阵为非奇异矩阵，即所谓的“小样本”问题。但是，在入脸识别中，由于人脸空间的维数非常大，而训练样本的个数有限，造成类内散射矩阵为奇异矩阵，从而使得用传统的求解方法失效。为了能在高维空间中求出MDA的最佳解，本文采用扰动的思想，巧妙地避开了矩阵的奇异性问题并找到了最佳变换矩阵。此外，把这一算法用于人脸的识别中，对ORL人脸图像库的实验显示，采用本文提出的算法达到比较低的错误率，其错误率仅为特征脸(Eigenface)方法的49．9％，为Fisher脸(Fisherface)的7914％。     

大尺度高分辨率遥感图像机场目标的快速识别

基于对机场的图像特征、目标特征的全面分析,提出了一种从大幅面高分辨率光学遥感图像(10000×10000 pixel)中快速识别机场目标的算法。首先通过预处理去除图像中大部分与目标特性不相关的区域,然后在剩余区域中作精确的检测,其中用到的目标特征有空间频率、均值、方差和能量等,使用canny边缘检测、线段分割与四邻域跟踪技术对目标进行精确识别。研究结果表明,本文算法能够实现对机场的快速识别,识别算法时间小于2 s。     

基于判别邻域嵌入的人脸识别

提出了一种人脸识别子空间方法:判别邻域嵌入(DNE).在框架中,训练样本数据的邻域和类关系被用来构建低维嵌入流形.在嵌入低维子空间后,同类样本保持它们固有的邻域关系,相反不同类近邻样本彼此远离.在ORL和Yale人脸数据库上,对提出的方法和主成分分析(PCA)、线性判别分析(LDA)、保持邻域嵌入(NPE)和保持局部投影(LPP)方法进行了比较,结果表明,提出的方法是有效的.     

Orthogonal laplacianfaces for face recognition.

Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm.     

DWT based HMM for face recognition

A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence ex- traction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available.     

Orthogonal isometric projection for face recognition

Isometric projection(IsoProjection) is a linear dimensionality reduction method,which explicitly takes into account the manifold structure embedded in the data.However,IsoProjection is non-orthogonal,which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality.The non-orthogonality also distorts the metric structure embedded in the data.This paper proposes a new method called orthogonal isometric projection(O-IsoProjection),which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection.Similar to IsoProjection,O-IsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space,and then obtains the projections by preserving such a graph structure.Different from IsoProjection,O-IsoProjection requires the basis vectors to be orthogonal,and the orthogonal basis vectors can be calculated by iterative way.Experimental results on ORL and Yale databases show that O-IsoProjection has better recognition rate for face recognition than Eigenface,Fisherface and IsoProjection.     

Learning an attention-aware parallel sharing network for facial attribute recognition

Existing multi-task learning based facial attribute recognition (FAR) methods usually employ the serial sharing network, where the high-level global features are used for attribute prediction. However, the shared low-level features with valuable spatial information are not well exploited for multiple tasks. This paper proposes a novel Attention-aware Parallel Sharing network termed APS for effective FAR. To make full use of the shared low-level features, the task-specific sub-networks can adaptively extract important features from each block of the shared sub-network. Furthermore, an effective attention mechanism with multi-feature soft-alignment modules is employed to evaluate the compatibility of the local and global features from the different network levels for discriminating attributes. In addition, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the problems of class imbalance and hard example mining for FAR. Experiments demonstrate that the proposed method achieves better performance than the state-of-the-art FAR methods.     

融合复小波与零空间判别投射的人脸识别方法

提出一种新颖的零空间判别投射(NDPE)的子空间人脸识别方法。基于局部保持映射(LPP)和非参数判别分析方法,NDPF能够同时编码人脸数据流形的几何和判别结构,并且通过在零空间中求解特征值来克服小样本尺寸问题。为进一步提高人脸识别的准确率,提出融合双树复小波变换(DTCWT)与NDPE的方法。实验结果表明,所提人脸识别方法在ORL、Yale和AR人脸数据库上均取得了较高的识别率。     

Research on two-dimensional lda for face recognition

The letter presents an improved two-dimensional linear discriminant analysis method for feature extraction. Compared with the current two-dimensional methods for feature extraction, the improved two-dimensional linear discriminant analysis method makes full use of not only the row and the column direction information of face images but also the discriminant information among different classes. The method is evaluated using the Nanjing University of Science and Technology （NUST） 603 face database and the Aleix Martinez and Robert Benavente （AR） face database. Experimental results show that the method in the letter is feasible and effective.     

适于人脸识别的PCA算法应用研究

人脸识别问题涵盖了多个领域如图像处理、计算机视觉和模式识别等,文中主要研究了PCA算法的原理,并基于MATLAB软件平台实现了人脸识别系统。本系统对图像进行预处理后,用ORL人脸库中的部分图像作为样本,通过K-L变换获取训练样本的特征值和特征向量,从而得到特征脸向量,然后将测试样本投影到已知的特征脸空间,计算出坐标系数,进而得出分类识别的结果。实验证明了本系统有较高的人脸识别率,具有一定的实际应用价值。     

A non-parameter bayesian classifier for face recognition

A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE) is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN) classifier in formation. The class conditional density is estimated by KDE and the bandwidth of the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspace analysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA) are respectively used to extract features, and the proposed method is compared with Probabilistic Reasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in face recognition systems. The experiments are performed on two benchmarks an.el the experimental results show that the KDE outperforms PRM, NC and NN classifiers.     

Sparsity adaptive matching pursuit for face recognition

Sparse representation methods have exhibited promising performance for pattern recognition. However, these methods largely rely on the data sparsity available in advance and are usually sensitive to noise in the training samples. To solve these problems, this paper presents sparsity adaptive matching pursuit based sparse representation for face recognition (SAMPSR). This method adaptively explores the valid training samples that exactly represent the test via iterative updating. Next, the test samples are reconstructed via the valid training samples, and classification is performed subsequently. The two-phase strategy helps to improve the discriminating power of class probability distribution, and thus alleviates effect of the noise from the training samples to some extent and correctly performs classification. In addition, the method solves the sparse coefficient by comparing the residual between the test sample and the reconstructed sample instead of using the sparsity. A large number of experiments show that our method achieves promising performance.     

Perfect histogram matching PCA for face recognition

We present an enhanced principal component analysis (PCA) algorithm for improving rate of face recognition. The proposed pre-processing method, termed as perfect histogram matching, modifies the image histogram to match a Gaussian shaped tonal distribution in the face images such that spatially the entire set of face images presents similar facial gray-level intensities while the face content in the frequency domain remains mostly unaltered. Computationally inexpensive, the perfect histogram matching algorithm proves to yield superior results when applied as a pre-processing module prior to the conventional PCA algorithm for face recognition. Experimental results are presented to demonstrate effectiveness of the technique.     

Multilinear discriminant analysis for face recognition.

There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we present a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction. Then, a novel approach, called k-mode optimization, is presented to iteratively learn these subspaces by unfolding the tensor along different tensor directions. We call this algorithm multilinear discriminant analysis (MDA), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes, 2) for classification problems involving higher order tensors, the MDA algorithm can avoid the curse of dimensionality dilemma and alleviate the small sample size problem, and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in k-mode optimization. We provide extensive experiments on ORL, CMU PIE, and FERET databases by encoding face images as second- or third-order tensors to demonstrate that the proposed MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes.     

Extended linear regression for undersampled face recognition

Linear Regression Classification (LRC) is a newly-appeared pattern recognition method, which formulates the recognition problem in terms of class-specific linear regression with sufficient training samples per class. In this paper, we extend LRC via intraclass variant dictionary and SVD to undersampled face recognition where there are very few, or even only one, training sample per class. Intraclass variant dictionary is adopted in undersampled situation to represent the possible variation between the training and testing samples. Three types of methods, quasi-inverse, ridge regularization and Singular Value Decomposition (SVD), are designed to solve low-rank problem of data matrix. Then the whole algorithm, named Extended LRC (ELRC), is presented for face recognition via intraclass variant dictionary and SVD. The experimental results on three well-known face databases show that the proposed ELRC has better generalization ability and is more robust to classification than many state-of-the-art methods in undersampled situation.