face-recognition

问题 To understand well the concept behind how Support Vector Machine works in MATLAB, I advised you to read briefly THIS VERY IMPORTANT LINK. I am trying to apply the same concept but I have different train set and test set. For more precision: My train set refers to be named as C2res{1} where this last is equal to: C2res{1} = 1.0e-05 * Columns 1 through 10 0.5341 0.5822 0.6185 0.7555 0.7369 0.7131 0.5985 0.6483 0.5668 0.6620 Columns 11 through 12 0.6523 0.6097 My test set refers to be named as

问题 Im trying to run face recognition and need a stream of images/files from the camera. I decided to use Flutters own camera library camera: 0.2.9+1 . The method CameraController.startImageStream(Function(CameraImage) onAvailable) seams promising but I can't figure out how to convert the CameraImage data into something readable for the face recognition. Have anyone else solved this? 回答1: CameraImage is normally in YUV 420 format. (Test cameraImage.format.group to confirm.) This works well with

问题 I want to follow the tutorials of the face recognition with opencv. But in the tutorial source code example getMat() method is used, which shows an error : class cv::face::FaceRecognizer has no member "getMat". After some searching I found out that opencv3.00 has deprecated that method. What can I use instead of getMat() now in opencv3.0? This is the tutorial I am trying to follow. 回答1: See the new API here. Replace model->getMat("eigenvalues") with model->getEigenValues() , model->getMat(

问题 Does anyone know the location of the face unlock code in the android source tree? I'm looking to include some face recognition in my app and would love to use the code that google has already created. I've already looked at the alternatives such as opencv and some other third party APIs, but I'd really just like to see the way that google did it. Thanks! 回答1: Unfortunately: Face unlock is closed-source google proprietary code, so we have no opportunity to modify it. Source: http://forum.xda

问题 I'm using eigenfaces (PCA) for face recognition in my code. I used the tutorials in OpenCV's website as a reference. While this works great for recognizing faces (ie it can tell you who is who correctly), the confidence-score based face verification (or imposter detection- verifying whether the face is enrolled in the training set) doesn't work well at all. I compute a Euclidean distance and use it as a confidence threshold. Are there any other ways I could calculate a confidence threshold? I

问题 I have installed opencv 3.1.0 with extra modules, but when I try to use >>> s = cv2.face.MinDistancePredictCollector() it returns an error Traceback (most recent call last): File "<pyshell#5>", line 1, in <module> s.MinDistancePredictCollector() AttributeError: 'module' object has no attribute 'MinDistancePredictCollector' I also checked the documentation: >>> help(cv2.face) Help on module cv2.face in cv2: NAME cv2.face FILE (built-in) FUNCTIONS StandardCollector_create(...) StandardCollector

问题 i'm working on face recognition project with my college. what i'm trying to take a snapshot and save it if the face is detected automatically before closing the webcam. what I have now is open cam and wait if face is detected and press "q" to take snapshot and save the image. Here is the code: import numpy as np import cv2 import time #import the cascade for face detection face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') def TakeSnapshotAndSave(): # access the

问题 I developed an application for face detection using OpenCVs HAAR cascade face detection. The algorithm works fine, however every once in a while It finds patterns on the wall or ather things that are not faces. I want to run additional checks on object suspected as faces but I want to do it only on objects that I am not confidant that they are faces. Is there a way to get a "confidence" level for a face detected by the HAAR cascade face detection? 回答1: OpenCV provides the confidence via the

问题 I've been working on a face recognition project using OpenCV's FaceRecognizer, doing gender differentiation. The algorithm works pretty well, but I wanted to implement some extra features into my program like the confidence of the prediction. The predict function can output a confidence level, but I'm not sure what it means. What does this confidence actually measure, and can I convert it into a percentage? int predictedLabel = -1; double confidence = 0.0; model->predict(face_resized,

问题 I've been working on a face recognition project using OpenCV's FaceRecognizer, doing gender differentiation. The algorithm works pretty well, but I wanted to implement some extra features into my program like the confidence of the prediction. The predict function can output a confidence level, but I'm not sure what it means. What does this confidence actually measure, and can I convert it into a percentage? int predictedLabel = -1; double confidence = 0.0; model->predict(face_resized,