face-recognition

问题 I'm trying to use the python dlib.train_shape_predictor function to train using a very large set of images (~50,000). I've created an xml file containing the necessary data, but it seems like train_shape_predictor loads all the referenced images into RAM before it starts training. This leads to the process getting terminated because it uses over 100gb of RAM. Even trimming down the data set uses over 20gb (machine only has 16gb physical memory). Is there some way to get train_shape_predictor

问题 Please explain how to calculate cumulative matching characteristic (cmc) curve? An example and some reading resources will be helpful? 回答1: CMC curve is a precision curve that provides detection or recognition precision for each rank. The horizontal line is rank of recognition, and the vertical line is precision percent. For example the first rank recognitions rate is indicated in first element, the second rank is indicated in second element and so on. As a result a non-decreasing curve will

问题 My System Configuration: Windows 10, Nvidia 940mx 2GB GDDR5 GPU, 8GB RAM, i5 8th generation. Software installed: CUDA toolkit 9.0 cuDNN 7.1.4 I have successfully installed dlib with GPU support after installing above requirements using commands below: $ git clone https://github.com/davisking/dlib.git $ python setup.py install --clean As stated by dlib's creater @Davis King, on my jupyter notebook I executed : import dlib dlib.DLIB_USE_CUDA [Out 17] :True Which verifies that my 'dlib' is using

问题 My System Configuration: Windows 10, Nvidia 940mx 2GB GDDR5 GPU, 8GB RAM, i5 8th generation. Software installed: CUDA toolkit 9.0 cuDNN 7.1.4 I have successfully installed dlib with GPU support after installing above requirements using commands below: $ git clone https://github.com/davisking/dlib.git $ python setup.py install --clean As stated by dlib's creater @Davis King, on my jupyter notebook I executed : import dlib dlib.DLIB_USE_CUDA [Out 17] :True Which verifies that my 'dlib' is using

问题 I am trying to make an eye-replacement program that can fill eyes with an image. For finding the eyes, I am using face_recognition by Ageitgey. However, the eye detection comes out very jagged. (I'm not talking about anti-aliasing, btw. I'll use super-sampling to solve that later) Here's my tiny bit of code: from PIL import Image, ImageDraw import face_recognition image = face_recognition.load_image_file("media/test_input_image.jpg") face_landmark_list = face_recognition.face_landmarks(image)

问题 confidence = 1.0f - sqrt( distSq / (float)(nTrainFaces * nEigens) ) / 255.0f Why it is divided by (nTrainFaces*nEigens)? 回答1: Why it is divided by (nTrainFaces*nEigens)? Well, if you're just trying to find the confidence value for the 'test face eigenvectors (or values?)' with just 1 and only trained face, then you'd do something like confidence = 1.0f - (sqrt( least_squared_distance / no_of_eigens ) / 255.0) However, since you're finding nearest neighbour within trained face database, you

问题 I am able to detect faces using open cv but I don't know how to process face recognition. I googled a lot but I didn't find any articles or blogs that can guide me. Can someone help me to develop face recognition App in android? 回答1: You can always use JavaCV that is a kind of wrapper for the native OpenCV functions: See: Face Recognition on Android In order to get everything working you have to extract some .so files to your libs folder in the project: Follow the instructions on this page Go

问题 I download and test JavaFaces as a java project in Windows. but i'am tring to use it in android framework , but BufferedImage is not available in android ! how can i make this project compatible with android ? here is FaceRec.java that contain errors about BufferedImage . public class FaceRec { private FaceBundle bundle; private double[][] weights; Handler fh; Logger logger = Logger.getLogger("facerecognition.javafaces.FaceRec"); public FaceRec() { try { fh = new FileHandler("facerec.log");

问题 I want to implement something like face recognition application using neural networks. So I found this incredible article. When we put the face image in the OpenFace network we get 128 measurements which we can use to compare faces. But here we get the main question: how we can quickly find the same face in the database with the closest 128 values? If we will use SVM(as it is written in the article) we should to retrain the classifier every time when we put the new face in the db, but it's

问题 I am developing an android app which uses fingerprint/face recognition for unlocking the app. I have successfully integrated fingerprint authentication using BiometricPrompt . But didn't know where to start for Face authentication. Any headsup will be really helpful. Also, Since BiometricPrompt comes with face, fingerprint, and iris, I don't want to use either MLKIT or any third party libraries. Below is the piece of code I used for Fingerprint authentication. new BiometricPrompt .Builder