face detection, eye detection,blink detection, and color tracking

最新推荐文章于 2025-01-02 13:00:00 发布
原创 最新推荐文章于 2025-01-02 13:00:00 发布 · 1.1k 阅读 · 1 ·
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#database #stream #video

MPT是一款跨平台工具箱，提供实时感知基础组件库，包括人脸识别、眼动检测、眨眼检测及颜色追踪等功能。此外，还介绍了实时人脸跟踪与识别的概念，即在视频流中定位人脸并匹配已知人脸数据库进行身份验证。
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MPT is a toolbox that supplies cross-platform librariesfor real-time perception primitives, including face detection, eye detection,blink detection, and color tracking.

 

http://sourceforge.net/projects/mptbox/

 

 

Real time face tracking and recognition

 

refers to the task of locating human faces in a video stream and identifying the faces by matching them against the database of known faces.

http://rtftr.sourceforge.net/

 

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openCV和python基于dlib库实现眨眼/睁闭眼检测--亲测高效1、检测方法（1）方法（2）眼睛纵横比（3）眨眼检测标准2、基于opencv的面部标志检测（1）软硬件需求（2）代码详解（3）眨眼检测效果3、常见问题（1） 参数设置问题（2）文件路径不对、摄像头未能启动 1、检测方法 （1）方法 与用于计算眨眼的传统图像处理方法不同，该方法通常涉及以下几种组合： 1、眼睛定位。 2、阈值找到眼睛的白色。 3、确定眼睛的“白色”区域是否消失了一段时间（表示眨眼）。 相反，眼睛长宽比是一种更为优雅的解决方
阅读《Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals》小结-含code
GuFrank的博客
07-25 362
摘要：提出了一种完全自动化和无监督的眨眼检测算法，Blink可以自我学习用户特定的眨眼脑电波轮廓。因此，Blink省去了任何用户的训练或手动检查的需求。Blink通过在单通道脑电上实现，能够精确估计眨眼开始和结束时间戳。.........
论文解读|Super-realtime facial landmark detection and shape fitting by deep regression...
Loong Cheng的博客
02-18 1450
资料 论文全名：Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters 代码地址： https://github.com/justusschock/shapenet 官方文档： https://shapenet.readthedocs.i...
3皮卡丘眨眼代码_眨眼检测调研以及活体检测应用
weixin_39987138的博客
12-12 376
眨眼作为一种无意识的生物特征，一般健康人眨眼间隔2s至10s，眼睛闭合持续时间100ms至400ms。利用健康人的眨眼频率特点，主要应用于疲劳检测和活体检测等两个场景。对于疲劳检测场景，疲劳特点是频繁眨眼（眨眼间隔变短）和长时间闭眼（眼睑闭合时间变长），算法实现主要是利用疲劳特点做文章。对于活体检测场景，对于纸质打印以及手机照片等静态人脸图像是不会有眨眼动作的，关注点是有没有眨眼动作，本人...
提取眨眼过程
qq_44465615的博客
01-30 853
这里我们使用opencv 和 dlib 库实现眨眼提取的过程 参考论文：Real-Time Eye Blink Detection using Facial Landmarks 在该片论文中，作者提出了一种提取几何特征——眼睛纵横比（eye aspect ratio (EAR)）——的方法，通过计算这个EAR的数值，我们可以判断眼睛是张开还是闭合，从而检测眨眼动作。 眼睛纵横比(EAR) 在讨论眼部纵横比之前，我们先来看一下面部特征点： 在dlib库中给出了具体实现。 从图中我们可以看到左眼和右眼分别对应
python中opencv怎么检测双眼_OpenCV/Python/dlib眨眼检测
weixin_39542043的博客
12-09 721
摘要：图像识别的新思路：眼睛纵横比，看看大牛如果用这种思路玩转识别眨眼动作！今天我们来使用面部标志和OpenCV检测和计算视频流中的眨眼次数。为了构建我们的眨眼检测器，我们将计算一个称为眼睛纵横比(EAR)的指标今天介绍的这个方法与传统的计算眨眼图像处理方法是不同的，使用眼睛的长宽比是更为简洁的解决方案，它涉及到基于眼睛的面部标志之间的距离比例是一个非常简单的计算。用OpenCV，Python...
eye blink detection
02-05
### 关于眼睑检测算法的实现 对于眼睑检测技术而言，一种常见的方法是利用机器学习模型来处理图像数据并识别眨眼动作。具体来说，在计算机视觉领域内，可以通过对面部特征点进行追踪进而判断眼睛的状态。 #### 基于面部标志点的方法 该方法依赖于预先训练好的人脸检测器以及 facial landmark predictor 来定位眼部区域的关键位置。一旦获取到这些坐标信息之后就可以计算两个眼角之间的距离变化情况从而推断出是否有眨眼行为发生[^1]。 ```python import dlib from skimage import io # 加载预训练的人脸检测器和landmark预测器 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') def calculate_eye_aspect_ratio(eye_points): """ 计算眼睛纵横比 """ A = distance.euclidean(eye_points[1], eye_points[5]) B = distance.euclidean(eye_points[2], eye_points[4]) C = distance.euclidean(eye_points[0], eye_points[3]) ear = (A + B) / (2.0 * C) return ear image_path = "path_to_image" img = io.imread(image_path) faces = detector(img, 0) for face in faces: landmarks = [(p.x, p.y) for p in predictor(img, face).parts()] left_eye = landmarks[36:42] right_eye = landmarks[42:48] # 判断是否闭眼逻辑... ``` 此代码片段展示了如何使用 `dlib` 库加载一个人脸检测器和一个用于提取面部特征点（包括双眼轮廓）的预测器。通过比较特定时间间隔内的EAR值可以有效地监测眨眼事件的发生[^2]。 另外值得注意的是，除了上述提到的传统CV方式外，还可以考虑采用深度神经网络来进行更精确的眼睛状态分类任务。例如卷积神经网络(CNNs)，其能够自动从大量样本中学习有用的表征特征而无需手动设计复杂的规则体系[^3]。
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