共查询到20条相似文献,搜索用时 15 毫秒
1.
《Optik》2014,125(22):6678-6680
Facial expression recognition plays an important role in a variety of real-world applications such as human–computer interaction, robot control, smart meeting, and visual surveillance. One critical step for facial expression recognition is to accurately extract emotional features. In this article, a facial expression recognition approach based on two-stage local facial textures extraction is proposed. At the first stage, we use the threshold local binary pattern to transform a facial image into a feature image. We then extract the most discriminate features from the feature image by using the block-based center-symmetric local binary pattern. Finally, these features are classified by the support vector machine. Experimental results are provided to illustrate the proposed approach is an effective method, compared to other similar methods. 相似文献
2.
A novel method is proposed for facial expression recognition combined curvelet transform with improved support vector machine (SVM) based on particle swarm optimization (PSO). The whole process is as follows. Firstly, as wavelet transform in two-dimension is good at isolating the discontinuities at edge points and only captures limited directional information, the curvelet transform is applied to extract facial expression feature substitutively. However, the amount of curvelet coefficients obtained in the first stage is too huge to be classified, therefore, all of the coefficients are sorted descendantly and the former larger 5 or 10% are remained while the others abandoned to reduce the dimension. Finally, PSO algorithm is employed to search for the reasonable parameters of SVM to increase classification accuracy. Experimental results demonstrate that our proposed method can form effective and reasonable facial expression feature, and achieve good recognition accuracy and robustness, which is competent for spirit states detection of operators to decrease defect rate of production. 相似文献
3.
4.
提出一种基于提升小波和Fisher线性判别法(FLD)相结合的人脸表情特征提取方法。提升小波是完全基于时空域的变换,具有多分辨率的特征,更有利于表情细节信息的提取,并且运算时间短,便于实现。图像经过提升小波变换后,取其低频分量和高频分量相结合作为整体特征,实验证明保存了绝大部分的表情分量,然后用Fisher线性判别法(FLD)进行特征提取,采用K-近邻法进行分类。在JAFFE数据库中,分辨率达到94.3%,识别时间为2.9s,证明了方法的有效性。 相似文献
5.
提出了一种基于Contourlet变换与FLD的人脸表情特征提取方法。Contourlet变换是一种新的多尺度几何分析方法,它在具备小波变换的多分辨率特性和时频局部特性的同时,还具有很强的多方向选择性和各向异性。图像经过Contourlet变换后的低频分量可体现表情的概貌,高频方向子带可体现表情的轮廓和纹理等细节。将低频分量与部分高频方向子带结合起来作为整体特征,可压缩图像的数据量,并能够体现表情的本质特征。首先用Fisher线性判别法(FLD)进行特征提取,然后采用K-近邻法进行分类。实验证明,该方法比经典的FLD方法有着更快的特征提取速度和更高的表情识别率。 相似文献
6.
In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error (RRE) of each class is the division of the l2-norm of reconstruction error to the l2-norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases. 相似文献
7.
为解决听力障碍者与无障碍者的信息交流问题,对哑语手势自动识别技术进行研究。提出了一种改进的手势识别算法。首先通过YUV肤色分割、图像差分、连通域检测等算法进行预处理,获取完整的手型区域图像。然后对手型的二值图像进行轮廓检测,采用LBP变换与主成分分析进行特征提取与压缩。最后运用支持向量机的机器学习算法构建分类器,对哑语手势进行分类识别。通过对630张手势图像进行实验,结果表明,提出的算法有效提高了识别率与速度,识别率达到94.22%,速度达到0.29s/幅,可以满足哑语交流的实时性要求。 相似文献
8.
Affine invariant feature extraction has been one of the key issues for object recognition, especially for the images captured under the variable environments. Considering that multiscale autoconvolution feature (MSA), which has the prominent comprehensive performance, is very sensitive to illumination change, a novel algorithm of extracting affine invariant feature is proposed based on the MSA transform combining with texture structure analysis. Firstly, a new MSA feature is extracted from texture structure map of the image which is computed based on local binary pattern theory. And then the original image based MSA and the texture map based MSA are combined to a new feature using generalized canonical correlation analysis, called TFMSA. This new feature represents much more image information than the traditional one and is performed in various object recognition tasks. The experimental results indicate that the new TFMSA not only conquers the defect of the traditional MSA, but also has good adaptability for a certain range of viewing angles, partial occlusion, uniform and non-uniform illumination changes. The recognition accuracy of the new feature is superior to MSA and other improved methods. 相似文献
9.
10.
An improved Richardson-Lucy algorithm based on local prior 总被引:2,自引:0,他引:2
Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson-Lucy (RL) algorithm succeeds in many motion deblurring processes, but the resulting images still contain visible ringing. When the estimated kernel is different from the real one, the result of the standard RL iterative algorithm will be worse. To suppress the ringing artifacts caused by failures in the blur kernel estimation, this paper improves the RL algorithm based on the local prior. Firstly, the standard deviation of pixels in the local window is computed to find the smooth region and the image gradient in the region is constrained to make its distribution consistent with the deblurring image gradient. Secondly, in order to suppress the ringing near the edge of a rigid body in the image, a new mask was obtained by computing the sharp edge of the image produced using the first step. If the kernel is large-scale, where the foreground is rigid and the background is smoothing, this step could produce a significant inhibitory effect on ringing artifacts. Thirdly, the boundary constraint is strengthened if the boundary is relatively smooth. As a result of the steps above, high-quality deblurred images can be obtained even when the estimated kernels are not perfectly accurate. On the basis of blurred images and the related kernel information taken by the additional hardware, our approach proved to be effective. 相似文献
11.
12.
13.
14.
Recently the sparse representation based classification (SRC) is successfully used to automatically recognize facial expression, well-known for its ability to solve occlusion and corruption problems. The results of those methods which using different features conjunction with SRC framework show state of the art performance on clean or noised facial expression images. Therefore, the role of feature extraction for SRC framework will greatly affect the success of facial expression recognition (FER). In this paper, we select a new feature which called LBP map. This feature is generated using local binary pattern (LBP) operator. It is not only robust to gray-scale variation, but also extracts sufficient texture information for SRC to deal with FER problem. Then we proposed a new method using the LBP map conjunction with the SRC framework. Firstly, we compared our method with state of the art published work. Then experiments on the Cohn–Kanade database show that the LBP map + SRC can reach the highest accuracy with the lowest time-consuming on clean face images than those methods which use different features such as raw image, Downsample image, Eigenfaces, Laplacianfaces and Gabor conjunction with SRC. We also experiment the LBP map + SRC to recognize face image with partial occluded and corrupted, the result shows that this method is more robust to occlusion and corruption than existing methods based on SRC framework. 相似文献
15.
Palmprint recognition method based on score level fusion 总被引:1,自引:0,他引:1
Different palmprint recognition methods have different advantages. The texture- and feature-based palmprint recognition methods can well exploit the minutiae of the palmprint but are not very robust to the possible variation such as the rotation and shift of the palm. The representation-based palmprint recognition method can well take advantage of the holistic information but seems not to be able to fully exploit the minutiae of the palmprint. In this paper, we propose to fuse the competitive coding method and two-phase test sample sparse representation (TPTSR) method for palmprint recognition. As one of representation-based methods, TPTSR method takes the whole palmprint image as the input and determines the contribution of the training samples of each class in representing the test sample. TPTSR also uses the contribution to calculate the similarities between the test sample and every class. The competitive coding method is a feature-based method and is highly complementary with TPTSR. We use a weighted fusion scheme to combine the matching scores generated from TPTSR and the competitive coding method. The experimental results show that the proposed method can obtain a very high classification accuracy and outperforms both TPTSR and the competitive coding method. 相似文献
16.
本文针对高速环境下的车型识别问题,提出基于方向可控滤波器的改进HOG算法。将方向可控滤波器算法与HOG算法相结合,以实现对车辆图像特征提取。采用主成分分析算法(PCA)约减特征向量维数以减少计算复杂度,利用支持向量机算法对提取特征进行样本训练,实现对车辆外型特征的识别。仿真实验结果表明:采用该算法原始车辆车型的识别正确率均值达到92.36%;另外,本文方法的识别速度比传统的HOG特征算法提高了3.45%,识别实时性得到提升。本文算法比传统HOG算法更优,能有效提高车型识别的效率。 相似文献
17.
18.
19.
为了增强网络对鸟鸣声信号的特征学习能力并提高识别精度,提出一种基于深度残差收缩网络和扩张卷积的鸟声识别方法。首先,提取鸟鸣声信号的对数梅尔特征及其一阶和二阶差分系数组成logMel特征集作为网络模型的输入;其次,通过深度残差收缩网络自动学习噪声阈值,减少噪声干扰;然后,引入扩张卷积增大卷积核感受野并利用注意力机制使网络更关注关键帧特征;最后,通过双向长短时记忆网络从学到的局部特征中学习长期依赖关系。以百鸟数据birdsdata鸟声库中的19种中国常见鸟类作为实验对象,识别正确率可以达到96.58%,并对比模型在不同信噪比数据下的识别结果,结果表明该模型在噪声环境下的识别效果优于现有模型。 相似文献
20.