Package org.opencv.face

Class EigenFaceRecognizer

    • Constructor Detail

      • EigenFaceRecognizer

        protected EigenFaceRecognizer​(long addr)

    • Method Detail

      • create

        public static EigenFaceRecognizer create​(int num_components,
                                         double threshold)
        Parameters:
        num_components - The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.
        threshold - The threshold applied in the prediction. ### Notes:
        • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
        • THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
        • This model does not support updating.
        ### Model internal data:
        • num_components see EigenFaceRecognizer::create.
        • threshold see EigenFaceRecognizer::create.
        • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
        • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
        • mean The sample mean calculated from the training data.
        • projections The projections of the training data.
        • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
        Returns:
        automatically generated

      • create

        public static EigenFaceRecognizer create​(int num_components)
        Parameters:
        num_components - The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. ### Notes:
        • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
        • THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
        • This model does not support updating.
        ### Model internal data:
        • num_components see EigenFaceRecognizer::create.
        • threshold see EigenFaceRecognizer::create.
        • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
        • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
        • mean The sample mean calculated from the training data.
        • projections The projections of the training data.
        • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
        Returns:
        automatically generated

      • create

        public static EigenFaceRecognizer create()
        Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. ### Notes:
        • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
        • THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
        • This model does not support updating.
        ### Model internal data:
        • num_components see EigenFaceRecognizer::create.
        • threshold see EigenFaceRecognizer::create.
        • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
        • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
        • mean The sample mean calculated from the training data.
        • projections The projections of the training data.
        • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
        Returns:
        automatically generated

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class BasicFaceRecognizer
        Throws:
        java.lang.Throwable