Low accuracy of SVM on Android

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Hello guys,

I have an Android project that uses the face detection feature (Cascade Classifier). After recognizing the face, I trim the eyes and use a descriptor (Kaze) in conjunction with Bag of Words and SVM to recognize open and closed eyes, left and right. I performed the SVM training with a set of 1600 images, 400 of each type.

The training is done in C#, and after finishing the training, I test with some images, and all are classified correctly.

However, when importing the resulting dictionary and svm files, using the camera results have been very bad.

Here are the images and files generated through training:

https://github.com/caiocanalli/OpenCV...

Attached, the C#, Java, and C ++ code I'm using.

Any suggestion of how to improve preaching and welcome.

Thank you.

public class Training
{
    private const string Path = "C:/Users/Administrator/Desktop/Data";

    private readonly KAZE _kaze;
    private readonly BFMatcher _bfMatcher;
    private readonly BOWKMeansTrainer _bowKMeansTrainer;
    private readonly BOWImgDescriptorExtractor _bowImgDescriptorExtractor;
    private readonly Dictionary<int, string> _images;

    private Mat _kazeDescriptors;
    private Mat _bowDescriptors;

    private Matrix<int> label;

    public Training()
    {
        int dictionarySize = 10000;

        _kaze = new KAZE(true, true, 0.00001F);
        _bfMatcher = new BFMatcher(DistanceType.L2);

        _bowKMeansTrainer = new BOWKMeansTrainer(
            dictionarySize,
            new MCvTermCriteria(200, 0.00001),
            1,
            KMeansInitType.PPCenters);

        _bowImgDescriptorExtractor =
            new BOWImgDescriptorExtractor(_kaze, _bfMatcher);

        _kazeDescriptors = new Mat();
        _bowDescriptors = new Mat();

        _images = new Dictionary<int, string>
        {
            { 1, "/closedLeftEyes" },
            { 2, "/openLeftEyes" },

            //{ 1, "/closedRightEyes" },
            //{ 2, "/openRightEyes" }
        };
    }

    public void Start()
    {
        // Compute KAZE descriptors

        System.Console.WriteLine("Compute KAZE descriptors...");

        foreach (var item in _images)
        {
            var files = Directory.GetFiles(
                Path + item.Value, "*.png");

            System.Console.WriteLine("Directory: " + item.Value + ". Size: " + files.Length);

            foreach (var file in files)
            {
                var image = new Image<Gray, byte>(file);

                Mat descriptors = new Mat();
                MKeyPoint[] keyPoints = _kaze.Detect(image);

                _kaze.Compute(image,
                    new VectorOfKeyPoint(keyPoints), descriptors);

                _kazeDescriptors.PushBack(descriptors);

                System.Console.WriteLine("Imagem: " + file);
            }
        }

        _bowKMeansTrainer.Add(_kazeDescriptors);

        // Cluster dictionary

        System.Console.WriteLine("Cluster dictionary...");

        Mat dictionary = new Mat();
        _bowKMeansTrainer.Cluster(dictionary);

        _bowImgDescriptorExtractor.SetVocabulary(dictionary);

        FileStorage fs = new FileStorage(
            "dictionary_left.xml", FileStorage.Mode.Write);
        fs.Write(dictionary, "dictionary");
        fs.ReleaseAndGetString();

        //FileStorage fs = new FileStorage(
        //    "dictionary_right.xml", FileStorage.Mode.Write);
        //fs.Write(dictionary, "dictionary");
        //fs.ReleaseAndGetString();

        // Compute BOW descriptors

        System.Console.WriteLine("Compute BOW descriptors...");

        var labels = new List<int>();

        foreach (var item in _images)
        {
            var files = Directory.GetFiles(
                Path + item.Value, "*.png");

            System.Console.WriteLine("Directory: " + item.Value + ". Size: " + files.Length);

            foreach (var file in files)
            {
                var image = new Image<Gray, byte>(file);

                Mat descriptors = new Mat();
                MKeyPoint[] keyPoints = _kaze.Detect(image);

                _bowImgDescriptorExtractor.Compute(image,
                    new VectorOfKeyPoint(keyPoints), descriptors);

                _bowDescriptors.PushBack(descriptors);
                labels.Add(item.Key);

                System.Console.WriteLine("Image: " + file);
            }
        }

        label = new Matrix<int>(labels.ToArray());

        // Train SVM

        System.Console.WriteLine("Train SVM...");

        SVM svm = new SVM();
        svm.SetKernel(SVM.SvmKernelType.Rbf);
        svm.Type = SVM.SvmType.CSvc;

        svm.TermCriteria = new MCvTermCriteria(400, 0.00001);

        TrainData trainData = new TrainData(
            _bowDescriptors, 
            Emgu.CV.ML.MlEnum.DataLayoutType.RowSample,
            label);

        System.Console.WriteLine("C: " + svm.C + " Gamma: " + svm.Gamma);

        bool result = svm.TrainAuto(trainData);

        System.Console.WriteLine("C: " + svm.C + " Gamma: " + svm.Gamma);

        svm.Save("svm_left.xml");
        //svm.Save("svm_right.xml");

        if (!result)
            throw new ...