Package org.opencv.face

Class FisherFaceRecognizer

    • Constructor Detail

      • FisherFaceRecognizer

        protected FisherFaceRecognizer​(long addr)

    • Method Detail

      • create

        public static FisherFaceRecognizer create​(int num_components,
                                          double threshold)
        Parameters:
        num_components - The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.
        threshold - The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. ### Notes:
        • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
        • THE FISHERFACES 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 FisherFaceRecognizer::create.
        • threshold see FisherFaceRecognizer::create.
        • eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
        • eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
        • mean The sample mean calculated from the training data.
        • projections The projections of the training data.
        • labels The labels corresponding to the projections.
        Returns:
        automatically generated

      • create

        public static FisherFaceRecognizer create​(int num_components)
        Parameters:
        num_components - The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically. is larger than the threshold, this method returns -1. ### Notes:
        • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
        • THE FISHERFACES 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 FisherFaceRecognizer::create.
        • threshold see FisherFaceRecognizer::create.
        • eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
        • eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
        • mean The sample mean calculated from the training data.
        • projections The projections of the training data.
        • labels The labels corresponding to the projections.
        Returns:
        automatically generated

      • create

        public static FisherFaceRecognizer create()
        Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically. is larger than the threshold, this method returns -1. ### Notes:
        • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
        • THE FISHERFACES 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 FisherFaceRecognizer::create.
        • threshold see FisherFaceRecognizer::create.
        • eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
        • eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
        • mean The sample mean calculated from the training data.
        • projections The projections of the training data.
        • labels The labels corresponding to the projections.
        Returns:
        automatically generated