使用Accord.NET SVM进行面部识别(MulticlassSupportVectorMachine) [英] Using an Accord.NET SVM for face recognition (MulticlassSupportVectorMachine)
问题描述
我正在使用OpenFace Windows二进制文件和Accord .NET创建
I am using OpenFace Windows binaries and Accord .NET to create a C# version of this Python-based face recognition system.
OpenFace可以完成大部分工作，我只需要训练SVM就可以使用已知面孔作为输出类对未知面孔(有概率)进行分类.
OpenFace does most of the work, I just need to train an SVM to classify an unknown face (with probability), using known faces as the output classes.
在此上下文中，面部"是一个包含面部测量值的CSV文件.理论上足够简单.似乎完成得最好，与单-vs-rest方法，我正在尝试从MulticlassSupportVectorMachine 示例在API中.
A "face" in this context is a CSV file full of face measurements. Simple enough in theory. As this seems best done with one-vs-rest methods, I am trying to work from the MulticlassSupportVectorMachine example in the API.
但是，据我所知，该示例使用其训练数据中的输入作为测试输入，因此我不确定训练在哪里结束并开始测试，但是我假设它位于调用.Decide()
However, as far as I can tell, that example uses the same inputs from its training data as the inputs for testing, so I'm not sure exactly where training ends and testing begins, but I assume its at the line that calls .Decide()
这就是我现在正在尝试的...
Here's what I'm trying now...
-创建一些简单的容器类
-Create a few simple container classes
public class Result
{
public int[] predictions;
public double[][] scores;
public double[][] probability;
public double error;
public double loss;
}
public class KnownFaces
{
public double[][] input;
public int[] output;
}
public class UnknownFaces
{
public double[][] input;
}`
获取已知面孔的编码
// Load encoding from CSV
double[] aiden1Encoding = RecognizeFace.GetEncodingFromCSV(Application.StartupPath + @"\of\processed\aiden1.csv");
double[] aiden2Encoding = RecognizeFace.GetEncodingFromCSV(Application.StartupPath + @"\of\processed\aiden2.csv");
...
double[] kate1Encoding = RecognizeFace.GetEncodingFromCSV(Application.StartupPath + @"\of\processed\kate1.csv");
double[] kate2Encoding = RecognizeFace.GetEncodingFromCSV(Application.StartupPath + @"\of\processed\kate2.csv");
...
//... etc (about 20 pictures of each person)
获取未知面孔的编码
double[] who1Encoding = RecognizeFace.GetEncodingFromCSV(Application.StartupPath + @"\of\processed\who1.csv");
double[] who2Encoding = RecognizeFace.GetEncodingFromCSV(Application.StartupPath + @"\of\processed\who2.csv");
...
将所有这些放到我的容器中
Put all of this in my containers
double[][] knownFacesFeatures = {
aiden1Encoding, aiden2Encoding, ...
kate1Encoding, kate2Encoding, ...
};
double[][] unknownFacesFeatures = {
who1Encoding, who2Encoding ...
};
RecognizeFace.KnownFaces knownFaces = new RecognizeFace.KnownFaces();
knownFaces.input = knownFacesFeatures;
RecognizeFace.UnknownFaces unknownFaces = new RecognizeFace.UnknownFaces();
unknownFaces.input = unknownFacesFeatures;
对已知面孔的输出进行分类
Classify the outputs of known faces
knownFaces.output = new int[]
{
0,0,0 ... // Aiden
1,1,1 ... // Kate
...
};
获得结果:
RecognizeFace.Result r = RecognizeFace.RecognizeFaces(knownFaces, unknownFaces);
...
public static Result RecognizeFaces(KnownFaces knownFaces, UnknownFaces unknownFaces)
{
Result toReturn = new Result();
// Create the multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning<Gaussian>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization<Gaussian>()
{
// Estimate a suitable guess for the Gaussian kernel's parameters.
// This estimate can serve as a starting point for a grid search.
UseKernelEstimation = true
}
};
// Learn a machine
var machine = teacher.Learn(knownFaces.input, knownFaces.output);
// Create the multi-class learning algorithm for the machine
var calibration = new MulticlassSupportVectorLearning<Gaussian>()
{
Model = machine, // We will start with an existing machine
// Configure the learning algorithm to use Platt's calibration
Learner = (param) => new ProbabilisticOutputCalibration<Gaussian>()
{
Model = param.Model // Start with an existing machine
}
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(knownFaces.input, knownFaces.output);
// Obtain class predictions for each sample
int[] predicted = machine.Decide(unknownFaces.input);
toReturn.predictions = predicted;
// Get class scores for each sample
double[][] scores = machine.Scores(unknownFaces.input);
toReturn.scores = scores;
// Get log-likelihoods (should be same as scores)
double[][] logl = machine.LogLikelihoods(unknownFaces.input);
// Get probability for each sample
double[][] prob = machine.Probabilities(unknownFaces.input);
toReturn.probability = prob;
//Compute classification error using mean accuracy (mAcc)
//double error = new HammingLoss(knownFaces.output).Loss(predicted);
//double loss = new CategoryCrossEntropyLoss(knownFaces.output).Loss(prob);
//toReturn.error = error;
//toReturn.loss = loss;
return toReturn;
}
问题是，如果我取消注释错误/丢失行，则会出现异常，
The problem is that if I uncomment the error / loss lines, I get an exception,
System.IndexOutOfRangeException: Index was outside the bounds of the array.
at Accord.Math.Optimization.Losses.HammingLoss.Loss(Int32[] actual)
at NETFaceRecognition.RecognizeFace.RecognizeFaces(KnownFaces knownFaces, UnknownFaces unknownFaces)
，如果我将它们保留在外，则代码会运行...并且不起作用.如果我迭代int []预测，则得到的结果通常是错误的(通常):
and if I leave them out, the code runs... and doesn't work. If I iterate int[] predictions, I get results that are simply wrong (usually):
Unknown Person #1's class: 2 Expected: 3 (Lena)
Unknown Person #2's class: 2 Expected: 3 (Lena)
Unknown Person #3's class: 2 Expected: 2 (James)
Unknown Person #4's class: 2 Expected: 2 (James)
Unknown Person #5's class: 3 Expected: (Unknown person, incorrect result expected)
Unknown Person #6's class: 0 Expected: 3 (Lena)
Unknown Person #7's class: 1 Expected: 2 (James)
Unknown Person #8's class: 3 Expected: 1 (Kate)
我的问题的要点是:我是否正确实现了类，而问题仍位于数据输入中，还是我误解了一些重要的东西?TIA
The gist of my questions is: Am I implementing the class correctly, with my problem residing somewhere in my data inputs, or have I misunderstood something important? TIA
推荐答案
我很确定您会收到错误消息，因为尺寸不匹配.此外，我认为您计算出的逻辑错误会因不适当的数据集而丢失.您可以在knownFaces上学习，并可以通过unknownFaces进行预测.
I'm pretty sure that you're getting the error because the dimensions don't match. Moreover, I think the logical mistake here you calc loses by inappropriate data sets. You learn on knownFaces and predict by unknownFaces.
double error = new HammingLoss(knownFaces.output).Loss(predicted);
您可能需要对未用于训练部分的数据的已知部分进行交叉验证.
You probably need to do cross validation on known part of data that not used in train part.
这样想吧:
double error = new HammingLoss(knownFacesForPredicted.output).Loss(predicted);
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