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[Special Effects] SVM_FACE DL : 0: 基于支持向量机的人脸检测训练集增强算法实现。根据支持向量机(support vector machine，简称SVM)~ ，对基于边界的分类算"~(geometric approach)~ 言，类别边界附近的样本通常比其他样本包含有更多的分类信息．基于这一基本思路，以人脸检测问题为例．探讨了对给定训练样本集进行边界增强的问题，并为此而提出了一种基于支持向量机和改进的非线性精简集算法 IRS(improved reduced set)的训练集边界样本增强算法，用以扩大-91l练集并改善其样本分布．其中，所谓IRS算法是指在精简集(reduced se0算法的核函数中嵌入一种新的距离度量一一图像欧式距离一一来改善其迭代近似性能，IRS 可以有效地生成新的、位于类别边界附近的虚拟样本以增强给定训练集．为了验证算法的有效性，采用增强的样本集训练基于AdaBoost的人脸检测器，并在MIT+CMU正面人脸测试库上进行了测试．实验结果表明通过这种方法能够有效地提高最终分类器的人脸检测性能．-According to support vector machines(SVMs)，for those geometric approach based classification methods，examples close to the class boundary usually are more informative than others．Taking face detection as an example，this paper addresses the problem of enhancing given training set and presents a nonlinear method to tackle the problem effectively．Based on SVM and improved reduced set algorithm (IRS)，the method generates new examples lying close to the face／non—face class boundary to enlarge the original dataset and hence improve its sample distribution．The new IRS algorithm has greatly improved the approximation performance of the original reduced set(RS)method by embedding a new distance metric called image Euclidean distance(IMED)into the keme1 function．To verify the generalization capability of the proposed method，the enhanced dataset is used to train an AdaBoost．based face detector and test it on the MIT+CMU frontal face test set．The experimental results show that the origina
Date : 2025-12-25 Size : 634kb User : 郭事业
[Special Effects] 53607890facedetection DL : 0: 人脸检测的研究具有重要的学术价值，人脸是一类具有相当复杂的细节变化的自然结构目标，对此类目标的挑战性在于：人脸由于外貌、表情、肤色等不同，具有模式的可变性；一般意义下的人脸上，可能存在眼镜、胡须等附属物；作为三维物体的人脸影像不可避免地受由光照产生的阴影的影响。因此，如果能够找到解决这些问题的方法，成功地构造出人脸检测系统，将为解决其他类似的复杂模式的检测问题提供重要的启示。-Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face detection can be regarded as a more general case of face localization. In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information. Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation), or both. The newer algorithms take into account variations in the image or video by factors such as f
Date : 2025-12-25 Size : 2.86mb User : 力量
[Special Effects] 10.1.1.70.5735 DL : 0: face recognition with frontal and profile view
Date : 2025-12-25 Size : 112kb User : hossain
[Special Effects] IMECS2010_pp130-133 DL : 0: This paper presents a novel adaptive algorithm to detect the center of pupil in frontal view faces. This algorithm, at first, employs the viola-Jones face detector to find the approximate location of face in an image. The knowledge of the face structure is exploited to detect the eye region. The histogram of the detected region is calculated and its CDF is employed to extract the eyelids and iris region in an adaptive way. The center of this region is considered as the pupil center. The experimental results show ninety one percent’s accuracy in detecting pupil center.
Date : 2025-12-25 Size : 708kb User : Gheis
[Special Effects] face_detection DL : 0: 本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。-The purpose of this paper is threefold: firstly, the local Successive Mean Quantization Transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up Sparse Network of Winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the Receiver Operation Characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors.
Date : 2025-12-25 Size : 1.49mb User : 吴绪周
[Special Effects] madical-image-registration DL : 0: 基于随机游走的额图像配准，在图像中定义种子点，然后寻找和该种子点最相似的位移，从而实现图像配准。-Frontal image registration based on random walk, define the seed point in the image, and then look for and the most similar to the seed point displacement, so as to realize image registration.
Date : 2025-12-25 Size : 340kb User : 曹德才
[Special Effects] Synthesis-method-frontal-face DL : 0: 正面人脸图像合成方法研究_张坤华 ,多张图片不同角度生成正脸，可以有效去除遮挡物-Study frontal face image synthesis method _ Zhang Kunhua, multiple pictures at different angles to generate a positive face, can effectively remove the obstruction
Date : 2025-12-25 Size : 1.2mb User : 王龙