diff --git a/lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp b/lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
index 24d67e18..1cb58dc2 100644
--- a/lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
+++ b/lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
@@ -537,7 +537,7 @@ void convolution_direct(std::vector<cv::Mat_<float> >& outputs, const std::vecto
 	int yB = height_in - height_k + 1;
 	int xB = width_n - width_k + 1;
 
-	cv::Mat_<float> input_matrix(input_maps.size() * height_k * width_k, yB * xB);
+	cv::Mat_<float> input_matrix(input_maps.size() * height_k * width_k + 1.0, yB * xB, 1.0f);
 
 	// Comibine im2col accross channels to prepare for matrix multiplication
 	for (size_t i = 0; i < input_maps.size(); ++i)
@@ -545,15 +545,13 @@ void convolution_direct(std::vector<cv::Mat_<float> >& outputs, const std::vecto
 		im2col_bias(input_maps[i], width_k, height_k, input_matrix(cv::Rect(0, i * height_k * width_k, yB * xB, height_k * width_k)));
 	}
 
-	// Actual multiplication
+	// Actual convolution (through multiplication)
 	cv::Mat_<float> out = weight_matrix * input_matrix;
 
 	// Move back to vectors and reshape accordingly (also add the bias)
 	for (size_t k = 0; k < out.rows; ++k)
 	{
-		cv::Mat_<float> reshaped = out.row(k) + biases[k];
-		reshaped = reshaped.reshape(1, yB);
-		outputs.push_back(reshaped);
+		outputs.push_back(out.row(k).reshape(1, yB));
 	}
 
 }
@@ -844,8 +842,19 @@ void CNN::Read(const string& location)
 						k_flat.copyTo(weight_matrix(cv::Rect(k, i * kernels_rearr[0][0].rows * kernels_rearr[0][0].cols, 1, kernels_rearr[0][0].rows * kernels_rearr[0][0].cols)));
 					}
 				}
+
 				// Transpose the weight matrix for more convenient computation
-				cnn_convolutional_layers_weights.push_back(weight_matrix.t());
+				weight_matrix = weight_matrix.t();
+
+				// Add a bias term to the weight matrix for efficiency
+				cv::Mat_<float> W(weight_matrix.rows, weight_matrix.cols + 1, 1.0);
+				for (size_t k = 0; k < weight_matrix.rows; ++k)
+				{
+					W.at<float>(k, weight_matrix.cols) = biases[k];
+				}
+				weight_matrix.copyTo(W(cv::Rect(0, 0, weight_matrix.cols, weight_matrix.rows)));
+
+				cnn_convolutional_layers_weights.push_back(W);
 
 			}
 			else if (layer_type == 1)