一种通用的红外人脸器官定位算法

红外人脸热像图是红外成像领域的研究热点之一,而对作为其基础性研究的通用红外人脸定位算法却研究的较少。利用由人脸生理结构造成的温度特异性和由温度变化形成的图像边缘,给出了一种通用的人脸器官定位算法。首先利用温度特异性定位鼻孔等温度特异性强的区域,然后以此为参考点,结合面部器官分布特点,对融合提取的图像边缘信息进行眼睛等其他器官的定位。实验结果表明,该方法对各器官的平均定位精度均达到了90%以上,具有一定的实用价值。另外,该方法不仅可以对常规的眼、鼻、口等器官进行定位,而且还可以对眉骨、瞳孔等位置进行定位。     

基于深度特征的快速人脸图像检索方法

针对计算机视觉领域的人脸图像检索问题,提出了一种基于深度特征的快速人脸图像检索方法。该方法首先使用人脸图像训练集对深度卷积神经网络模型进行人脸分类训练;在此基础上采用三元组损失方法对已训练好的人脸分类网络模型进行微调,使得网络能够更加有效地提取人脸特征构建高效的特征向量进行人脸检索初步过滤;最后,为了进一步提高系统检索性能,提出一阶段查询扩展方法对待检索人脸图像特征向量进行融合加强。在两个公用人脸数据集(CASIA-3DFaceV1和Labeled Faces in the Wild dataset)上进行详尽的实验验证,结果表明,基于深度特征的人脸图像检索方法不仅能够显著提高检索结果的准确率,而且该方法简单可靠,能够快速地实现人脸检索任务。     

基于PCA/ICA和误差补偿算法的眼镜摘除研究

眼镜遮挡对人脸图像识别率影响较大,为了提高戴眼镜人脸图像的识别率,需要摘除正面人脸图像中眼镜。采用PCA和ICA算法提取了二阶统计信息、高阶信息,较好地刻画了人脸的细节特征;同时结合灰度补偿算法,弥补了由于戴眼镜图像未参与特征空间的训练过程中所造成的合成图像中人脸表情的失真;利用反复的迭代补偿算法进行人脸重建。通过Yale人脸数据库进行仿真实验结果表明:该方法合成的图像没有眼镜的痕迹,看起来更加自然,有效地改善了戴眼镜图像的面部特性,提高了人脸的识别率。     

基于循环反向编码的三维人脸测量方法

针对人脸测量时的抖动现象，设计了一种循环反向编码方法。该方法无需专门投影反向二值条纹辅助边缘点定位，减少了投影图案的数量。用循环的三帧条纹图像代替原本利用正反两帧条纹图像定位的方式，提高边缘点检测精度的同时能够有效消除定位偏差。实验表明，所提方法能够有效提高测量速度，同时保持较高的测量精度，减少点云中的运动波纹。     

弱光照环境下人脸表情图像识别方法研究

对弱光照环境下人脸表情图像进行识别,可以更好地对人类的情感进行分类,有利于人类在现实社会中的沟通。当前方法利用提取人脸表情图像的一维特征完成对弱光照环境下人脸表情图像的识别,该方法无法对人脸表情图像进行详细地描述,导致人脸表情图像在识别时经常出现识别精度低、速度慢的问题。为此,提出一种基于BP神经网络的弱光照环境下人脸表情图像识别方法。该方法首先利用自相似性对带有噪声的图像进行图像区域划分,并依据统计学习获得线性空间,通过对空间的投影获得不含噪声的人脸表情图像区域向量,将人脸表情图像进行重组,得到去噪后的图像,然后利用Cabor变换对人脸表情图像特征进行提取,采用AdaBoost对弱分类器以及人脸表情图像样本进行训练,并通过多次弱分类器的迭代,得到最终的人脸表情图像强分类器,完成对弱光照环境下人脸表情图像的识别。实验结果证明,所提方法可以提高人脸表情图像的识别准确率,加快识别速度,为该领域的研究发展提供强有力依据。     

基于方向滤波和自商图像的人脸光照补偿

为了实时处理恶劣光照条件下的人脸图像,在自商图像的基础上,提出了一种新的人脸光照补偿的方法.该方法首先在人脸三维光照简化模型的基础上,利用方向滤波削弱附着阴影.然后结合高斯低通滤波器,进一步削弱投射阴影.两者非线性结合,显著改善光照图像的质量.利用YaleB数据库提供的光照图像,在10万人脸数据库系统中进行测试,结果表明在光照条件恶劣的情况下能显著提高识别率.     

基于方向滤波和自商图像的人脸光照补偿

为了实时处理恶劣光照条件下的人脸图像,在自商图像的基础上,提出了一种新的人脸光照补偿的方法.该方法首先在人脸三维光照简化模型的基础上,利用方向滤波削弱附着阴影.然后结合高斯低通滤波器,进一步削弱投射阴影.两者非线性结合,显著改善光照图像的质量.利用Yale B数据库提供的光照图像,在10万人脸数据库系统中进行测试,结果表明在光照条件恶劣的情况下能显著提高识别率.     

利用几何特性及神经网络进行人脸探测技术的研究

在人脸识别过程中 ,首先也是最重要的一个环节是人脸探测 ,因为一旦从图像中定位并提取到了人脸 ,那么下一步的人脸识别工作就变得非常容易。眼睛是人脸图像中最容易探测的部位 ,而且通过探测双眼来发现人脸最符合人的视觉习惯。提出了一种基于几何特征分析和人工神经网络的由粗到细的两级人脸探测方法。在第一级中 ,眼睛和脸是通过测量眼睛的尺寸和眼睛与脸的位置关系探测到的 ,第一级的输出是一个尺寸归一化的人脸 ,但偶尔也伴随着一个或多个因对复杂背景中与眼睛类似的物体的误判而得到的非人脸图像 ;第二级神经网络正是用来过滤掉第一级中被误判的人脸。实验表明 ,这种由粗到细的两级人脸探测系统具有很高的稳定性和探测正确率     

基于小波分解系数的贝叶斯人脸识别方法

本文给出了贝叶斯人脸识别方法中匹配准则的多个近似表达式及一种实用的快速计算方法,在此基础上,利用反对称双正交小波变换的微分算子功能,提出了一种利用两幅人脸图像的小波变换系数差作为模式矢量的贝叶斯人脸识别方法,并利用AR人脸图象库进行了实验,实验结果表明本文方法与基于图像灰度的类似方法相比,识别率提高8％左右,此外本文方法也提供了一条在图像压缩数据域中实现人脸识别的可能途径。     

融合心率变异性与表情的非接触心理压力检测

针对现有心理压力检测方法主观性强、准确率低,且无法连续监测的问题,提出了一种融合心率变异性(HRV)与人脸表情的非接触式心理压力检测方法.该方法通过成像式光电容积描记(IPPG)技术从视频图像中提取HRV信息,并通过VGG19网络建立表情识别模型,获得人脸表情.将HRV及表情共同作为特征输入,利用支持向量机进行训练分类...     

Robust face recognition based on sparse representation in 2D Fisherface space

Sparse representation is being proved to be effective for many tasks in the field of face recognition. In this paper, we will propose an efficient face recognition algorithm via sparse representation in 2D Fisherface space. We firstly transformed the 2D image into 2D Fisherface in preprocessing, and classify the testing image via sparse representation in the 2D Fisherface space. Then we extend the proposed method using some supplementary matrices to deal with random pixels corruption. For face image with contiguous occlusion, we partition each image into some blocks, and define a new rule combining sparsity and reconstruction residual to discard the occluded blocks, the final result is aggregated by voting the classification result of the valid individual block. The experimental results have shown that the proposed algorithm achieves a satisfying performance in both accuracy and robustness.     

A novel virtual samples-based sparse representation method for face recognition

A limited training set usually limits the performance of face recognition in practice. Even sparse representation-based methods which outperform in face recognition cannot avoid such situation. In order to effectively improve recognition accuracy of sparse representation-based methods on a limited training set, a novel virtual samples-based sparse representation (VSSR) method for face recognition is proposed in this paper. In the proposed method, virtual training samples are constructed to enrich the size and diversity of a training set and a sparse representation-based method is used to classify test samples. Extensive experiments on different face databases confirm that VSSR is robust to illumination variations and works better than many representative representation-based face recognition methods.     

Face recognition based on PCA and logistic regression analysis

Face recognition is an important research hotspot. More and more new methods have been proposed in recent years. In this paper, we propose a novel face recognition method which is based on PCA and logistic regression. PCA is one of the most important methods in pattern recognition. Therefore, in our method, PCA is used to extract feature and reduce the dimensions of process data. Afterwards, we present a novel classification algorithm and use logistic regression as the classifier for face recognition. The experimental results on two different face databases are presented to illustrate the efficacy of our proposed method.     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

基于数字图像处理技术的人脸检测算法研究

为了提高人脸检测的准确性及检测速度,需要对基于数字图像处理技术的人脸检测算法进行研究。使用当前方法进行人脸检测时,需要提取脸部特征数目较多、检测速度过慢,降低人脸检测效率。为此,提出一种基于数字图像处理技术的人脸检测算法。该方法首先获取人脸数字图像,通过拉开数字图像的灰度间距,使数字图像灰度均匀分布,进而提高数字图像对比度,使图像更加清晰,再通过Wiener维纳滤算法对处理后的数字图像进行图像平滑去噪,在此基础上使用Robert边缘检测算子方法对数字图像人脸边缘每个像素点检测,得到数字图像中人脸边缘的基本图像,将其输入到计算机数字图像处理系统中进行识别检测。实验仿真证明,所提算法在检测速度及准确性等方面具有明显的优势。     

Color balloon snakes for face segmentation

In this paper, a new color balloon snake model is introduced and used for face segmentation in color images. It is an extension of existing balloon snake models. Based on a coarse detection of facial features, the method combines a skin-tone distribution model and a boundary diffusion model to search for the facial boundary. The skin distribution is a single Gaussian, which is proposed to extract the skin-tone region in the RGB space. The diffusion model, which is invented to diffuse the facial boundary, is a one-dimensional Gauss revolution surface. The parameters are evaluated based on an AdaBoost face detection method. The color snakes are weighted by the distributions, and the external forces evolve dynamically to reach the boundary, which depends on the balance between the internal and external forces. Experiments were conducted, and the results show that the model provides desired segmentation outcomes. It is robust against complex backgrounds and lighting pollution.     

Integrating the original face images and “symmetrical faces” to perform face recognition

Using the original and ‘symmetrical face’ training samples to perform representation based face recognition was first proposed in [1]. It simultaneously used the original and ‘symmetrical face’ training samples to perform a two-step classification and achieved an outstanding classification result. However, in [1] the “symmetrical face” is devised only for one method. In this paper, we do some improvements on the basis of [1] and combine this “symmetrical faces” transformation with several representation based methods. We exploit all original training samples, left “symmetrical face” training samples and right “symmetrical face” training samples for classification and use the score fusion for ultimate face recognition. The symmetry of the face is first used to generate new samples, which is different from original face image but can really reflect some possible appearance of the face. It effectively overcomes the problem of non-sufficient training samples. The experimental results show that the proposed scheme can be used to improve a number of traditional representation based methods including those that are not presented in the paper.     

Thermal 3D modeling system based on 3-view geometry

In this paper, we propose a novel thermal three-dimensional (3D) modeling system that includes 3D shape, visual, and thermal infrared information and solves a registration problem among these three types of information. The proposed system consists of a projector, a visual camera and, a thermal camera (PVT). To generate 3D shape information, we use a structured light technique, which consists of a visual camera and a projector. A thermal camera is added to the structured light system in order to provide thermal information. To solve the correspondence problem between the three sensors, we use three-view geometry. Finally, we obtain registered PVT data, which includes visual, thermal, and 3D shape information. Among various potential applications such as industrial measurements, biological experiments, military usage, and so on, we have adapted the proposed method to biometrics, particularly for face recognition. With the proposed method, we obtain multi-modal 3D face data that includes not only textural information but also data regarding head pose, 3D shape, and thermal information. Experimental results show that the performance of the proposed face recognition system is not limited by head pose variation which is a serious problem in face recognition.