diff --git a/modules/contrib/include/opencv2/contrib/contrib.hpp b/modules/contrib/include/opencv2/contrib/contrib.hpp
index 9f8ed9d52c99ef8575ebddf13fb6fcdbd58f95f6..049c82211e5748e589c80869679647252980453b 100644
--- a/modules/contrib/include/opencv2/contrib/contrib.hpp
+++ b/modules/contrib/include/opencv2/contrib/contrib.hpp
@@ -861,18 +861,7 @@ namespace cv
         // Optimization Criterion on given data in src and corresponding labels
         // in labels. If 0 (or less) number of components are given, they are
         // automatically determined for given data in computation.
-        LDA(const Mat& src, vector<int> labels,
-                int num_components = 0) :
-                    _num_components(num_components)
-        {
-            this->compute(src, labels); //! compute eigenvectors and eigenvalues
-        }
-
-        // Initializes and performs a Discriminant Analysis with Fisher's
-        // Optimization Criterion on given data in src and corresponding labels
-        // in labels. If 0 (or less) number of components are given, they are
-        // automatically determined for given data in computation.
-        LDA(InputArray src, InputArray labels,
+        LDA(InputArrayOfArrays src, InputArray labels,
                 int num_components = 0) :
                     _num_components(num_components)
         {
@@ -895,7 +884,7 @@ namespace cv
         ~LDA() {}
 
         //! Compute the discriminants for data in src and labels.
-        void compute(InputArray src, InputArray labels);
+        void compute(InputArrayOfArrays src, InputArray labels);
 
         // Projects samples into the LDA subspace.
         Mat project(InputArray src);
@@ -915,7 +904,7 @@ namespace cv
         Mat _eigenvectors;
         Mat _eigenvalues;
 
-        void lda(InputArray src, InputArray labels);
+        void lda(InputArrayOfArrays src, InputArray labels);
     };
 
     class CV_EXPORTS_W FaceRecognizer : public Algorithm
diff --git a/modules/contrib/src/facerec.cpp b/modules/contrib/src/facerec.cpp
index 6ff51fe89ad2c60f8d8853c972ee4a73968fd83c..8c93906b42a9f59b5c182bb3ccbf3d070e70823e 100644
--- a/modules/contrib/src/facerec.cpp
+++ b/modules/contrib/src/facerec.cpp
@@ -464,9 +464,12 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
     // clear existing model data
     _labels.release();
     _projections.clear();
-    // get the number of unique classes (provide a cv::Mat overloaded version?)
+    // safely copy from cv::Mat to std::vector
     vector<int> ll;
-    labels.copyTo(ll);
+    for(unsigned int i = 0; i < labels.total(); i++) {
+        ll.push_back(labels.at<int>(i));
+    }
+    // get the number of unique classes
     int C = (int) remove_dups(ll).size();
     // clip number of components to be a valid number
     if((_num_components <= 0) || (_num_components > (C-1)))
diff --git a/modules/contrib/src/lda.cpp b/modules/contrib/src/lda.cpp
index 22ca97c8f665c4cb2b73f11a4ce37ce67713bb36..b099177dff1d1a750f01e020e4a2571a914b871e 100644
--- a/modules/contrib/src/lda.cpp
+++ b/modules/contrib/src/lda.cpp
@@ -975,10 +975,17 @@ void LDA::load(const FileStorage& fs) {
     fs["eigenvectors"] >> _eigenvectors;
 }
 
-void LDA::lda(InputArray _src, InputArray _lbls) {
+void LDA::lda(InputArrayOfArrays _src, InputArray _lbls) {
     // get data
     Mat src = _src.getMat();
-    vector<int> labels = _lbls.getMat();
+    vector<int> labels;
+    // safely copy the labels
+    {
+        Mat tmp = _lbls.getMat();
+        for(unsigned int i = 0; i < tmp.total(); i++) {
+            labels.push_back(tmp.at<int>(i));
+        }
+    }
     // turn into row sampled matrix
     Mat data;
     // ensure working matrix is double precision
@@ -1078,7 +1085,7 @@ void LDA::lda(InputArray _src, InputArray _lbls) {
     _eigenvectors = Mat(_eigenvectors, Range::all(), Range(0, _num_components));
 }
 
-void LDA::compute(InputArray _src, InputArray _lbls) {
+void LDA::compute(InputArrayOfArrays _src, InputArray _lbls) {
     switch(_src.kind()) {
     case _InputArray::STD_VECTOR_MAT:
         lda(asRowMatrix(_src, CV_64FC1), _lbls);