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Using PCAand LDA for dimensionality reduction for SVM

I am preparing data for training an SVM. I use PCA to reduce dimensionality of data before feeding into SVM as shown in code below.

    Mat trainData; //Hold data for training. Each row is a sample    
    vector<Mat> histograms; //Contains row histograms of LBP features
    convertVectorToMat(histograms, trainData); //Convert vector of Mat to Mat (40 rows, 4096 columns)
    PCA pca(trainData, Mat(), PCA::DATA_AS_ROW, (classes - 1));//PCA gives (40 rows, 39 columns)
    Mat mean = pca.mean.reshape(1, 1);

    //Project data to PCA feature space
    Mat projection = pca.project(trainData);

    //Perform LDA on data projected on PCA feature space
    LDA lda((classes - 1));
    lda.compute(projection, labels);
    Mat_<float> ldaProjected = lda.project(projection);
    normalize(ldaProjected, ldaProjected, 0, 1, NORM_MINMAX, CV_32FC1);

I am passing Mat ldaProjected to SVM for training together with corresponding labels for training. My question is am I doing it right or I should have passed Mat projection to SVM. I whichever case, SVM is only giving same class label for any sample I predict. Kindly advice if am preparing my data well for training. I intended to use LDA for dimensionality reduction for training multi-class SVM.

Using PCAand PCA and LDA for dimensionality reduction for SVM

I am preparing data for training an SVM. I use PCA to reduce dimensionality of data before ~~feeding~~ using LDA for class discriminant dimensionality reduction. I then feed reduced data projected into LDA subspace to SVM as shown in code below.

    Mat trainData; //Hold data for training. Each row is a sample    
    vector<Mat> histograms; //Contains row histograms of LBP features
    convertVectorToMat(histograms, trainData); //Convert vector of Mat to Mat (40 rows, 4096 columns)
    PCA pca(trainData, Mat(), PCA::DATA_AS_ROW, (classes - 1));//PCA gives (40 rows, 39 columns)
    Mat mean = pca.mean.reshape(1, 1);

    //Project data to PCA feature space
    Mat projection = pca.project(trainData);

    //Perform LDA on data projected on PCA feature space
    LDA lda((classes - 1));
    lda.compute(projection, labels);
    Mat_<float> ldaProjected = lda.project(projection);
    normalize(ldaProjected, ldaProjected, 0, 1, NORM_MINMAX, CV_32FC1);

I am passing Mat ldaProjected to SVM ~~for training~~ together with corresponding labels for training. My question is am I doing it right or I should have passed Mat projection to SVM. I In whichever case, SVM is only giving same class label for any sample I predict. Kindly advice if am preparing my data well for training. I intended to use LDA for dimensionality reduction for training multi-class SVM.