搜索资源列表
Proposed_code
- Image classification using RBFSVM and Manhattan distance. It detects face and eye. Face detection using Viola jones.
irisdetectionmatlabcoding
- This useful matlab code for the eye position and detection and serial communication. Used in eye controlled wheel chair-This is useful matlab code for the eye position and detection and serial communication. Used in eye controlled wheel chair
aam-matlab
- ASM算法 MATLAB源代码 人脸特征点定位的目的是在人脸检测的基础上，进一步确定脸部特征点（眼睛、眉毛、鼻子、嘴巴、脸部外轮廓）的位置。定位算法的基本思路是：人脸的纹理特征和各个特征点之间的位置约束结合。-The purpose of the facial feature point positioning is to further determine the position of the facial feature points (eye, eyebrows, nose, mou
Image-processing-source-code
- 图像处理源代码，基于Hough变换的人眼虹膜定位方法，基于Kalman滤波的目标跟踪，基于模糊集的图像增强方法，基于蚁群算法的图像边缘检测。-Image processing source code, Hough transform based human eye iris location method, Kalman filter based target tracking, fuzzy set based image enhancement method, ant colony algor
source-code-to-vessel-detection-in-eye-retina
- concept building in matlab
IrisDetector-object-oriented
- Eye pupils detection using webcam
Intelligent DC power supply_embedded_code
- 智能直流供电及功耗表基于SLH89F5162单片机，是由输入的电源作为源头，经过开关控制电路，再到输出采集电路，最后到输出端口，形成完整的供电及检测和保护链路，为设备提供有保障的直流电源。 所有采集的数据，以及配置信息，均显示在一个带中文的12864液晶屏上，实时让使用者看到数据，并且可对电压、电流的值进行限制，配置过压过流值，同时也可以在液晶上配置定时开启和关闭的时间。除了液晶的显示，还具有蜂鸣器、LED的醒目提示功能，能够直观快速的判断当前运行情况。(Intelligent DC power
source-code-to-vessel-detection-in-eye-retina
- source code diabetic retinopathy
openface-master
- toolbox for facial landmark detection, head pose estimation, facial action unit recognition, eye-gaze estimation
肤色-眼睛识别
- 运用色彩空间皮肤判定和眼睛定位的人脸检测(Face detection using color space decision and eye location)
MATLAB疲劳检测系统[自己视频]
- 本设计为基于MATLAB的疲劳检测识别，可应用于疲劳驾驶监测，专注度检测等应用。本设计带有GUI可视化界面，自行录制好视频后，读取视频，分帧，读取每一帧影像，计算其眼睛张合度及嘴巴张合度，通过这2个参数来判断是否疲劳。(This design is based on MATLAB fatigue detection and recognition, can be applied to fatigue driving monitoring, concentration detection and
深度学习mtcnn
- 用市面上的摄像头，可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and n