A real time implementation of a pose invariant face recognition engine with a novel face segmentation algorithm

In this paper we propose a fast and efficient algorithm for segmenting a face suitable for recognition from a video sequence. We first obtain a coarse face region using skin color, then using dynamic template matching the face is efficiently segmented at varying scale and pose in real time. We have also developed and tested some heuristics which localizes only the face region, even when subjects are wearing skin color dress. The segmented face is then handed over to a recognition algorithm based on principal component analysis and linear discriminant analysis. The on-line face detection, segmentation and recognition algorithm takes an average of 0.06 sec on a 3.2 GHz P4 machine.     

Quaternion-based improved LPP method for color face recognition

In recent years, pattern recognition and computer vision have increasingly become the focus of research. Locality preserving projection (LPP) is a very important learning method in these two fields and has been widely used. Using LPP to perform face recognition, we usually can get a high accuracy. However, the face recognition application of LPP suffers from a number of problems and the small sample size is the most famous one. Moreover, though the face image is usually a color image, LPP cannot sufficiently exploit the color and we should first convert the color image into the gray image and then apply LPP to it. Transforming the color image into the gray image will cause a serious loss of image information. In this paper, we first use the quaternion to represent the color pixel. As a result, an original training or test sample can be denoted as a quaternion vector. Then we apply LPP to the quaternion vectors to perform feature extraction for the original training and test samples. The devised quaternion-based improved LPP method is presented in detail. Experimental results show that our method can get a higher classification accuracy than other methods.     

云计算环境下人脸表情智能识别改进技术研究

随着人脸识别技术的开发,对于如何提高人脸表情智能识别改进技术的研究也越来越多;如何提高人脸识别的准确度和完整度是当前发展的主要需要,而计算机云计算功能在人脸识别中的应用在一定程度上解决了此问题;通过改进细菌觅食算法,再将其应用到主要成分分析算法对图像基本特征进行提取分析;通过以上的算法输入计算机网络云储存当中,实现云计算技术在人脸识别中的应用;文章将通过对于算法部署函数的办法进行图片解析工作,并且利用智能人脸识别软件对图像进行抽丝、分类、匹配等工作进行功能状态进行测试;实验结果表明利用云计算技术通过连接网络云计算系统可以对目前的人脸识别以及分类做到更高的准确性和适应性。     

基于核独立成分分析的人脸识别

研究一种基于核独立成分分析的人脸识别方法。利用支持向量机的核函数思想,将原始人脸图像向量映射到高维特征空间,然后在高维特征空间中进行独立成分分析(ICA),提取非线性独立成分作为特征向量进行分类识别。实验结果表明该方法要比常规的基于ICA和PCA的人脸识别算法的识别率要高。     

一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法

 为了克服光照、表情变化等因素对人脸识别的影响,本文提出了一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法.该方法首先把每幅人脸图像经过Gabor小波变换后得到的40个不同尺度和方向下的图像都看作是独立的样本,再把不同人脸中的同一尺度和方向的变换结果进行特征重组,得到40个独立地新特征矩阵.为了增强对光照、表情变化的鲁棒性,每一新特征矩阵的识别贡献被本文所提出的自适应权重方法计算得到.其次,对每一新特征矩阵采用离散余弦变化进行降维,并采用了鉴别力量分析方法来选取最有鉴别力的离散余弦变换系数作为特征向量.最后,抽取线性鉴别分析特征进行识别.大量的实验证明了本文所提方法的有效性.     

基于多变量鉴别分析的光学畸变不变性图像识别

提出一种基于鉴别分析的光学畸变不变性图像识别方法：对包含各种畸变的训练图像集采用主成分分析得到若干本征图像,作为参考模式与测试图像做光学相关,利用本民输入图像的相关结果作为识别特征,采用最佳鉴别分析做了训练和识别,即可实现对输入图像的畸变为性快速识别。采用非相干光相关器为光学实现硬件,给出了实验结果。     

Color component 3D Arnold transform for polychromatic pattern recognition

We propose a novel method for polychromatic pattern recognition based on color component 3D Arnold transform. Three color components (for example RGB) are first transformed into three chaotic images by the use of 3D Arnold transform. And then any one of the chaotic images is chosen as the input image of the JTC to be recognized. As a result, strong color discrimination capability is achieved and common color images as well as some special color images can be recognized, while still preserving compact system and easier analysis of the output. Numerical results demonstrate the feasibility and effectiveness of the proposed method.     

Polychromatic pattern recognition based on color component chaos encoding

In order to overcome the disadvantages which the single-channel system and the multichannel system suffer from some special cases, such as, when there exists a linear relationship between the corresponding color components of the target image and the reference image, a novel method for color pattern recognition is proposed based on color component chaos encoding. In this approach, the color components are first encoded into chaos images and then the encoded images serve as the input images of the single-channel (or multichannel) system. As a result, the color difference resulting from the linear relationship between the corresponding color components of the target image and the reference image can be recognized. Computer simulations prove that this method is valid.     

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.     

Nonparametric subspace analysis fused to 2DPCA for face recognition

Two-dimensional principal component analysis (2DPCA) is one of the representative techniques for image representation and recognition. However, keen storage requirements and computational complexity consist in 2DPCA. Meanwhile, the performance of 2DPCA is delicate in illumination variations. Nonparametric subspace analysis (NSA) is a subspace learning method that can reduce dimensionality and identify local information for discrimination, so that it can make 2DPCA perform well in illumination. Motivated by above facts, 2DPCA fused with NSA is implemented for face recognition, which can reduce dimensions of the 2DPCA feature vectors and enhance the contribution of principal components to face recognition. Experiments carried out on ORL, Yale B, and FERET facial databases show that valid recognition rates can be achieved by the proposed method compared to 2DPCA, 2DPCA plus PCA, LDA methods and demonstrate promising abilities against illumination variations.