Description: matlab人脸识别,能精准识别照片中的人脸，并且精确识别出人脸五官，给出照片库，能进行对比从而找出人脸。(Matlab face recognition, can accurately identify the face in the photo, and accurately identify the facial features, give a photo library, can be compared to find out the face.) Platform: |
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Author:帅哥.. |
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Description: 人脸特征提取matlab源码。适用于人脸识别的matlab实现。(Facial feature extraction matlab source code. It is suitable for matlab implementation of face recognition.) Platform: |
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Author:ZCzhuang |
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Description: OpenPose人体姿态识别项目是美国卡耐基梅隆大学（CMU）基于卷积神经网络和监督学习并以caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人，具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用，基于它的实例如雨后春笋般涌现。人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景，人们更加熟悉的应用就是抖音尬舞机(OpenPost Human Attitude Recognition Project is an open source library developed by Carnegie Mellon University (CMU) based on convolutional neural network and supervised learning and caffe framework. Posture estimation such as human motion, facial expression and finger movement can be realized. It is suitable for single person and multi-person, and has excellent robustness. It is the first real-time multi-person two-dimensional attitude estimation application based on deep learning in the world. Examples based on it have sprung up like mushrooms after a spring rain. Human posture estimation technology has broad application prospects in sports fitness, motion acquisition, 3D fitting, public opinion monitoring and other fields. People are more familiar with the application of tremolo embarrassing dance machine.) Platform: |
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Author:对对对对的 |
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Description: 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 network layers, so that each layer can learn more features and greatly improve the generalization ability. The network is deeper, the amount of calculation in each layer is reduced, and the ability of feature extraction is strengthened, so as to improve the accuracy of target classification. On the LFW data set, the accuracy of face recognition reaches 98.40%. In this experiment, mtcnn is introduced into the face detection algorithm. Its backbone network is divided into three convolutional neural networks: p-net, R-Net and o-net. Among them, o-net is the most strict in screening candidate face frames. It will output the coordinates of a human face detection frame and five facial feature points (left eye, right eye, nose, left mouth corner, right mouth corner).) Platform: |
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Author:莱尼 |
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Search - facial recognition