Multi-face detection based on downsampling and modified subtractive clustering for color images

This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.

A color based face detection system using multiple templates

A color based system using multiple templates was developed and implemented for detecting hu-man faces in color images.The algorithm comsists of three image processing steps.The first step is human skin color statistics.Then it separates skin regions from non-skin regions.After that,it locates the frontal human face(s) within the skin regions.In the first step,250 skin samples from persons of different ethnicities are used to determine the color distribution of human skin in chromatic color space in order to get a chroma chart showing likelihoods of skin colors.This chroma chart is used to generate,from the original color image,a gray scale image whose gray value at a pixel shows its likelihood of representing the shin,The algorithm uses an adaptive thresholding process to achieve the optimal threshold value for dividing the gray scale image into sep-arate skin regions from non skin regions.Finally,multiple face templates matching is used to determine if a given skin region represents a frontal human face or not.Test of the system with more than 400 color images showed that the resulting detection rate was 83%,which is better than most colou-based face detection sys-tems.The average speed for face detection is 0.8 second/image(400×300pixels) on a Pentium 3(800MHz) PC.

A ciosed-loop algorithm to detect human face using color and reinforcement learning

A closed-loop algorithm to detect human face using color information and reinforcement learning is presented in this paper. By using a skin-color selector, the regions with color "like" that of human skin are selected as candidates for human face. In the next stage, the candidates are matched with a face model and given an evaluation of the match degree by the matching module. And if the evaluation of the match result is too low, a reinforcement learning stage will start to search the best parameters of the skin-color selector. It has been tested using many photos of various ethnic groups under various lighting conditions, such as different light source, high light and shadow. And the experiment result proved that this algorithm is robust to the vary-ing lighting conditions and personal conditions.

Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features

Face detection is considered as a challenging problem in the field of image analysis and computer vision. There are many researches in this area, but because of its importance, it needs to be further developed. Successive Mean Quantization Transform (SMQT) for illumination and sensor insensitive operation and Sparse Network of Winnow (SNoW) to speed up the original classifier based face detection technique presented such a good result. In this paper we use the Mean of Medians of CbCr (MMCbCr) color correction approach to enhance the combined SMQT features and SNoW classifier face detection technique. The proposed technique is applied on color images gathered from various sources such as Internet, and Georgia Database. Experimental results show that the face detection performance of the proposed method is more effective and accurate compared to SFSC method.

Face Detection and Localization in Color Images: An Efficient Neural Approach

Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks is given. Second, and maybe most importantly, an adaptive cubic-spline neural network is designed to be used to detect and locate human faces in uncontrolled environments. The experimental results conducted on our test set show the effectiveness of the proposed approach and it can compare favorably with other state-of-the-art approaches in the literature.

基于I-ConvNeXt的GAN生成人脸图像鉴别方法

为鉴别社交网络中人脸图像的真假,在ConvNeXt基础上提出一种针对生成式对抗网络(Generative Adversarial Networks,GAN)生成人脸图像的鉴别方法。该方法以ConvNeXt网络结构为主体,利用人脸图像的颜色特征和空间纹理特征,采用多颜色空间多通道组合输入(Multichannel Input,MCI),扩大网络的学习范围;同时引入通道注意力机制和空间注意力机制来凸显真假人脸图像在颜色分量和纹理特征上的差异,进而实现生成人脸图像和真实人脸图像的检测与识别。实验结果表明,使用改进后的ConvNeXt(Improved ConvNeXt,I-ConvNeXt)网络结构对GAN生成人脸图像的识别准确率达到了99.405%,与原ConvNeXt算法相比,平均准确率提高了1.455个百分点。该结果验证了所提方案的可行性、合理性。

基于FPGA的疲劳驾驶检测硬件加速器设计

随着交通工具大幅使用,交通事故频繁发生,疲劳驾驶是导致交通事故的一项重要原因。该设计采用硬件加速器来处理OV7725摄像头采集的视频流数据,制定肤色阈值来搜索人脸区域的边界,实现视频流实时动态标识人脸,通过人脸特征在阈值内的累计时长来判断疲劳级别并及时预警。得益于硬件加速器的并行处理优势,使得疲劳检测和预警更加快速稳定。该文完成了基于FPGA的人脸识别算法和疲劳驾驶检测的硬件加速器设计,仿真并验证了功能,对性能进行了测试,结果证明可实现快速准确的三级疲劳驾驶判断,并证明了该方案的可行性。

基于MATLAB的人脸图像分割算法研究

对基于MATLAB的人脸图像分割算法进行研究,利用颜色空间的不同色度特性,将给定的人脸图像按照不同色度划定的取值范围进行人脸和背景的分割。给出了三种颜色空间的基本原理,包括RGB颜色空间,HSV颜色空间,YCrCb颜色空间,对RGB颜色空间转换到HSV颜色空间和YCrCb颜色空间进行描述,并采用MATLAB软件完成图像的颜色空间转换。基于两种颜色空间完成不同类型人脸图像的图像分割,并对分割效果进行对比分析。

基于形状-色彩的改进的Laplacianface人脸识别算法

针对人脸识别算法中普遍存在的鲁棒性不高的问题,提出一种新颖的特征提取手段,使提取的特征相对于图像尺度,人脸姿态等条件具有不变性;同时,将特征提取算法集成至Laplacianface人脸识别算法中,形成一种改进的基于形状-色彩特征的人脸识别算法。实验结果表明算法不仅提高了现有人脸识别算法的准确度,而且在人脸姿态等条件发生变化的情况下仍然能保持较高的识别率,有效提高了算法的鲁棒性。

改进肤色模型和光流算法的人脸检测方法

人脸检测具有很高的实际应用价值,例如在身份识别、视频监控等领域的应用也越来越广泛,其检测的方法多种多样。为解决视频序列中的人脸检测问题,总结了肤色模型和光流算法的优缺点,融合二者优势,提出了改进的基于肤色模型和光流算法的人脸检测算法。该方法在一定程度上解决了人脸旋转、部分遮挡、表情变化和光照对人脸检测和跟踪的影响,具有较强的鲁棒性。

Face Detection under Complex Background and Illumination

For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.

Efficient Approach for Face Detection in Video Surveillance

Security access control systems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable processes.In this paper,an approach is presented to detect faces in video surveillance.Firstly,both the skin-color and motion components are applied to extract skin-like regions.The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm.Secondly,the image is clustered into separated face candidates by using the region growing technique.Finally,the face candidates are further verified by the rule-based algorithm.Experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.

ROBUST FACE DETECTION AND ANALYSIS

This paper presents a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. The two maps and local similarity map are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. The experimental results demonstrate its validity and robustness to identify faces under certain variations.

复合黏度改性剂对C60清水混凝土性能的优化分析

自主研发了一种复合黏度改性剂(CVMA),研究了掺量为0~15%的CVMA对C60桥塔清水混凝土新拌性能、硬化性能和外观质量的影响。结果表明:掺10%的CVMA可降低清水混凝土倒置坍落度筒排空时间56.9%,并提高了混凝土的力学和耐久性能;掺入CVMA可以改善清水混凝土的气泡稳定性、优化气泡结构,从而减少清水混凝土的外观气泡缺陷,同时可抑制色差缺陷,达到清水混凝土外观质量要求。

接结方式对双面全显色提花织物的呈色影响

针对分层组合设计模式下由于接结方式的改变造成全显色组织设计的调整现象,是否对双面提花织物表面呈色产生影响的问题,进行双面织物样品色彩实验验证。通过相同组织循环R、组织点加强数E的组合全显色组织和可遮盖全显色组织设计,相同的双面织物参数设置,织造自身接结、附加纬接结、添加附加纬未接结的3组双面织物样品,进行3组织物样品的色彩范围、色彩过渡均匀性和色差对比实验。结果表明:虽然组合全显色组织和可遮盖全显色组织之间2R/E个灰度级差造成接结方式不同时双面提花织物表面的呈色范围具有可接受及更轻微程度的差别,但在色彩过渡均匀性方面并未产生明显影响,并且附加纬接结方式本身也不会对双面提花织物表面色彩产生明显影响,设计师可根据产品需要自由选择接结方式。

现浇彩色装饰清水混凝土建筑施工关键技术

以浙江某高校图书馆“红、黄、灰、白”四色同体的现浇装饰清水混凝土工程为研究对象,总结了适用于现浇彩色装饰清水混凝土建筑的施工技术,包括抗裂保护体系、钢模板表面喷砂处理工艺、模板二次封闭施工技术、不同色彩清水混凝土界面的拼接技术等,可为现浇彩色装饰清水混凝土的推广应用提供技术支撑。

自密实混凝土钢管柱超声检测内部缺陷研究

【目的】研究自密实混凝土钢管柱的内部缺陷及界面黏结检测的定量分析。【方法】采用超声波无损检测方法检测分析C60自密实混凝土钢管柱缺陷情况,以首波声时探测超声波传递路径,提出波速异常值判断方法,根据预埋缺陷情况将波速小于4.5 km/s的测点判定为异常点;通过作3D波速和缺陷高度颜色映射曲面图判断缺陷所在位置;通过各测点声时差,运用几何方法定量分析缺陷大小。【结果】研究表明:钢管壁对内部混凝土超声波参数基本无影响;首波声时越短,混凝土密实度越高,当钢管柱内混凝土存在缺陷时,声时变长,波幅变小,首波和相邻波振幅较小;各平面波速异常区域与预制缺陷位置基本吻合,最大缺陷尺寸为233 mm,其所在位置对应预埋立方体与球型缺陷位置处;当超声波速小于4.0 km/s时,缺陷高度超过50 mm,可初步判定混凝土中存在较大缺陷或脱空。计算出的缺陷大小与实际缺陷相差较小,表明超声无损检测技术可运用于工程实践。