Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.     

Illumination Invariant Face Recognition Using Quaternion-Based Correlation Filters

Existing face recognition systems decrease their performance when face images are affected by lighting variations. Recently, several quaternionic representations of face image features and a quaternion-based correlation filter have been combined in order to cope with the effects of having non-properly illuminated face images. The use of this approach has the advantage of using only one training face image per person. In this paper, the original idea based on the unconstrained optimal trade-off quaternion filter (UOTQF) is extended and two additional different correlation filters in quaternionic domain are evaluate: a phase only quaternion filter (POQF) and a separable trade-off quaternion filter (STOQF). Three different quaternion-based correlation filters are designed and conjugated with four face feature extraction methods aiming at obtaining the best combination: a two-level discrete wavelet decomposition (DWT), image differentiation (DIF), discrete cosine transform (DCT) and local binary patterns (LBP). Verification and identification experiments confirms that when combining a quaternionic representation with a quaternion-based correlation filter, both with good discriminative power and illumination invariant properties, an improvement in face recognition accuracy is obtained.     

运用模糊积分进行信息融合的人脸识别方法

提出一种运用模糊积分的原理对整体和局部特征进行融合的人脸识别方法.在实际应用中,现有人脸识别系统缺乏对外界环境进行自适应调节的能力,为此,首先将人脸的关键特征点眼睛,鼻子和嘴巴进行分割,接着采用Fisherface方法对人脸图象进行特征提取和压缩,并建立了三个基于局部特征和一个基于整体特征的分类器,最后利用模糊积分的思想对这些分类器进行融合,将融合后的结果用于人脸识别中.试验表明:该方法能够有效的结合人脸图像的互补信息,提高了识别率.     

图像多模态扰动的人脸识别方法

为了克服因人脸图像检测引起的配准不稳定性和小样本引起的维数灾难,由一副二维人脸图像通过上下左右平移生成4个图像,把生成的图像与原来的图像一起加入训练样本集,构成新的训练图像集。基于二维图像,结合图像局部结构信息,设计了准则函数,获得双投影矩阵,抽取人脸特征。对待识别人脸图像,由它的扰动图像设计识别方法。与传统的人脸识别方法相比,该方法的识别效果更好;Yale和ORL人脸数据库上的实验结果验证了该方法的有效性。     

基于图像边缘小波矩和支持向量机的目标识别

小波矩结合了矩特征和小波特征,既反映了图像的全局性信息,又反映了图像的局域性信息,并且具有旋转、平移和缩放不变性.利用小波矩与支持向量机进行目标识别,不但解决了图像识别中特征量随图像旋转、平移和缩放而变化的问题,而且提高了对近似物体的识别能力,是解决小样本、近似图像识别的有效方法.     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced Discriminant Analysis

Most face recognition techniques have been successful in dealing with high-resolution (HR) frontal face images. However, real-world face recognition systems are often confronted with the low-resolution (LR) face images with pose and illumination variations. This is a very challenging issue, especially under the constraint of using only a single gallery image per person. To address the problem, we propose a novel approach called coupled kernel-based enhanced discriminant analysis (CKEDA). CKEDA aims to simultaneously project the features from LR non-frontal probe images and HR frontal gallery ones into a common space where discrimination property is maximized. There are four advantages of the proposed approach: 1) by using the appropriate kernel function, the data becomes linearly separable, which is beneficial for recognition; 2) inspired by linear discriminant analysis (LDA), we integrate multiple discriminant factors into our objective function to enhance the discrimination property; 3) we use the gallery extended trick to improve the recognition performance for a single gallery image per person problem; 4) our approach can address the problem of matching LR non-frontal probe images with HR frontal gallery images, which is difficult for most existing face recognition techniques. Experimental evaluation on the multi-PIE dataset signifies highly competitive performance of our algorithm.      

Hierarchical fusion of multi-spectral face images for improved recognition performance

This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved.     

融合全局和局部特征的人脸识别算法

针对人脸识别算法缺乏对光照变化的自动调节能力的弱点,提出了一种综合利用全局和局部特征进行人脸识别的新方法。对整幅人脸图像进行主成分分析;针对人脸局部特征,提出一种根据各局部子块包含的信息量即利用图像熵值进行自动加权的算法;基于贝叶斯原理对全局和局部特征进行数据融合,给出最终结果。实验结果表明,该数据融合算法综合全局和局部特征提取方式的优势,有效提高了人脸识别率。     

Hybrid-boost learning for multi-pose face detection and facial expression recognition

This paper proposes a hybrid-boost learning algorithm for multi-pose face detection and facial expression recognition. To speed-up the detection process, the system searches the entire frame for the potential face regions by using skin color detection and segmentation. Then it scans the skin color segments of the image and applies the weak classifiers along with the strong classifier for face detection and expression classification. This system detects human face in different scales, various poses, different expressions, partial-occlusion, and defocus. Our major contribution is proposing the weak hybrid classifiers selection based on the Harr-like (local) features and Gabor (global) features. The multi-pose face detection algorithm can also be modified for facial expression recognition. The experimental results show that our face detection system and facial expression recognition system have better performance than the other classifiers.     

小波变换结合Cycle Spinning图像融合的研究

正交小波变换对平移具有敏感性,直接使用其进行变换会产生伪吉布斯现象,Cycle Spinning方法在此基础上产生.但目前文献中较少使用该方法进行图像融合的研究.结合小波变换和Cycle Spinning循环平移的思想,提出一种基于小波变换的Cycle Spinning图像融合方法.实验证明该方法与传统DWT方法相比,能有效地保留图像细节信息,获得更好的视觉效果,更高的PSNR值和更低的RMSE值.     

基于子模式的Gabor特征融合的单样本人脸识别

针对传统人脸识别方法在单训练样本条件下效果不佳的缺点,提出基于子模式的Gabor特征融合方法并用于单样本人脸识别。首先采用Gabor变换抽取人脸局部信息,为有效利用面部器官的空间位置信息,将Gabor人脸图像分块构成子模式,采用最小距离分类器对各子模式分类。最后对各子模式分类结果做决策级融合得出分类结果。根据子模式构成原则和决策级融合策略不同,提出两种子模式Gabor特征融合方法。利用ORL人脸库和CAS-PEAL-R1人脸库进行实验和比较分析,实验结果表明文中方法有效提高单样本人脸识别的正确率,改善单样本人脸识别系统的性能。     

视频人脸识别中判别性联合多流形分析

将基于视频的人脸识别转换为图像集识别问题,并提出两种流形来表示每个图像集:一种是类间流形,表示每个图像集的平均脸信息;另一种是类内流形,表示每个图像集的所有原始图像的信息.类间流形针对图像集之间的区别提取整体判别信息,作用是选出几个与待识别图像集较为相似的候选图像集.类内流形则考虑图像集内各原始图像之间的关系,负责从候选图像集中找出最为相似的一个.不同于现有的非线性流形方法中每幅图像对应流形中的一个点,采用分片技术学习两种流形的投影矩阵,每个分片对应流形中的一个点,所学到的特征更具有判别性,进而使流形边界更加清晰,同时解决了传统非线性流形方法中的角度偏差和不充分采样问题.还提出了与分片技术相匹配的流形之间的距离度量方法.最后在几个广为研究的数据集上进行了实验,结果表明:新方法的识别准确率高,尤其适用于不受控环境下的视频识别,而且不受视频段长短的影响.     

基于相关性和有效互补性分析的多分类器组合方法

定义了分类器组合中的相关向量和有效互补性的概念,并提出了一种新的组合准 则,即最大有效互补准则.对人脸图象作正交小波变换,得到它在不同频带上的四个子图象, 然后分别提取奇异值特征.实验表明,这四组特征之间以及相应的分类结果之间的相关性都 较小,组合结果明显优于原始图象的奇异值特征的分类效果,并优于常用的组合方法--计 分法的效果.     

Improved segmentation of semiconductor defects using area sieves

This paper aims at investigating a novel non-referential solution to the problem of defect detection on semiconductor wafer-die images. The suggested solution focuses on segmenting defects from the images using wavelet transformation and morphology-related properties of the associated wavelet coefficients. More specifically, a novel methodology is investigated for segmenting defects by applying an area sieves technique to innovative multidimensional wavelet-based features. These features are extracted from the original defective image using the non-reference K-Level 2-D DWT (Discrete Wavelet Transform). The results of the proposed methodology are illustrated in defective die images where the defective areas are segmented with higher accuracy than the one obtained by applying other reference-based feature extraction methodologies. The first uses all the wavelet coefficients derived from the K-Level 2-D DWT, while the second one uses area sieves to segment the defective regions. Both methods involve in the same classification stage as the proposed feature extraction approach. The promising results obtained outline the importance of judicious selection and processing of 2-D DWT wavelet coefficients for industrial pattern recognition applications.     

基于小波域PCA与LDA相结合的红外人脸识别方法*

提出了一种新的小波域主元分析与线性辨别分析相结合的红外人脸识别方法。首先通过DWT将红外人脸图像通过二级小波分解成七个子带,舍去两次分解中的对角子带,对剩下的五个子带进行有效的组合;然后用PCA方法对组合后的向量进行特征提取,再把PCA提取的特征向量进行线性辨别分析;最后用欧氏距离和三近邻分类器得到分类结果。同传统的PCA和PCA LDA的方法相比,该方法更能利用人脸图像的有用判别信息,并得到更好的识别效果。     

Variable lighting face recognition using discrete wavelet transform

This paper presents a new discrete wavelet transform (DWT) based illumination normalization approach for face recognition under varying lighting conditions. Our method consists of three steps. Firstly, DWT-based denoising technique is employed to detect the illumination discontinuities in the detail subbands. And the detail coefficients are updated with using the obtained discontinuity information. Secondly, a smooth version of the input image is obtained by applying the inverse DWT on the updated wavelet coefficients. Finally, multi-scale reflectance model is presented to extract the illumination invariant features. The merit of the proposed method is it can preserve the illumination discontinuities when smoothing image. Thus it can reduce the halo artifacts in the normalized images. Moreover, only one parameter involved and the parameter selection process is simple and computationally fast. Experiments are carried out upon the Yale B and CMU PIE face databases, and the results demonstrate the proposed method can achieve satisfactory recognition rates under varying illumination conditions.