org.openimaj.image.processing.face.recognition.EigenFaceRecogniser Maven / Gradle / Ivy

Go to download
/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   * 	Redistributions of source code must retain the above copyright notice,
 * 	this list of conditions and the following disclaimer.
 *
 *   *	Redistributions in binary form must reproduce the above copyright notice,
 * 	this list of conditions and the following disclaimer in the documentation
 * 	and/or other materials provided with the distribution.
 *
 *   *	Neither the name of the University of Southampton nor the names of its
 * 	contributors may be used to endorse or promote products derived from this
 * 	software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.image.processing.face.recognition;

import org.openimaj.data.dataset.GroupedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.experiment.dataset.util.DatasetAdaptors;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.DoubleFVComparator;
import org.openimaj.feature.FVProviderExtractor;
import org.openimaj.image.processing.face.alignment.FaceAligner;
import org.openimaj.image.processing.face.detection.DetectedFace;
import org.openimaj.image.processing.face.feature.EigenFaceFeature.Extractor;
import org.openimaj.ml.annotation.IncrementalAnnotator;
import org.openimaj.ml.annotation.basic.KNNAnnotator;

/**
 * Implementation of a {@link FaceRecogniser} based on Eigenfaces. Any kind of
 * machine learning implementation can be used for the actual classification.
 * 
 * @author Jonathon Hare ([email protected])
 * 
 * @param 
 *            Type of {@link DetectedFace}
 * @param 
 *            Type of object representing a person
 */
public class EigenFaceRecogniser
		extends
		LazyFaceRecogniser>
{
	protected EigenFaceRecogniser() {
	}

	/**
	 * Construct with the given feature extractor and underlying
	 * {@link FaceRecogniser}.
	 * 
	 * @param extractor
	 *            the feature extractor
	 * @param internalRecogniser
	 *            the face recogniser
	 */
	public EigenFaceRecogniser(Extractor extractor,
			FaceRecogniser internalRecogniser)
	{
		super(extractor, internalRecogniser);
	}

	/**
	 * Construct with the given feature extractor and underlying
	 * {@link IncrementalAnnotator}.
	 * 
	 * @param extractor
	 *            the feature extractor
	 * @param annotator
	 *            the annotator
	 */
	public EigenFaceRecogniser(Extractor extractor,
			IncrementalAnnotator annotator)
	{
		this(extractor, AnnotatorFaceRecogniser.create(annotator));
	}

	/**
	 * Convenience method to create an {@link EigenFaceRecogniser} with a
	 * standard KNN classifier, incorporating a threshold on the maximum
	 * distance (or minimum similarity) to allow a match.
	 * 
	 * @param 
	 *            The type of {@link DetectedFace}
	 * @param 
	 *            the type representing a person
	 * @param numComponents
	 *            the number of principal components to keep
	 * @param aligner
	 *            the face aligner
	 * @param k
	 *            the number of nearest neighbours
	 * @param compar
	 *            the distance comparison function
	 * @param threshold
	 *            a distance threshold to limit matches.
	 * @return a new {@link EigenFaceRecogniser}
	 */
	public static 
			EigenFaceRecogniser create(int numComponents, FaceAligner aligner, int k,
					DoubleFVComparator compar, float threshold)
	{
		final Extractor extractor = new Extractor(numComponents, aligner);
		final FVProviderExtractor extractor2 = FVProviderExtractor.create(extractor);

		final KNNAnnotator knn =
				KNNAnnotator.create(extractor2, compar, k, threshold);

		return new EigenFaceRecogniser(extractor, knn);
	}

	/**
	 * Convenience method to create an {@link EigenFaceRecogniser} with a
	 * standard KNN classifier.
	 * 
	 * @param 
	 *            The type of {@link DetectedFace}
	 * @param 
	 *            the type representing a person
	 * @param numComponents
	 *            the number of principal components to keep
	 * @param aligner
	 *            the face aligner
	 * @param k
	 *            the number of nearest neighbours
	 * @param compar
	 *            the distance comparison function
	 * @return a new {@link EigenFaceRecogniser}
	 */
	public static 
			EigenFaceRecogniser create(int numComponents, FaceAligner aligner, int k,
					DoubleFVComparator compar)
	{
		final Extractor extractor = new Extractor(numComponents, aligner);
		final FVProviderExtractor extractor2 = FVProviderExtractor.create(extractor);

		final KNNAnnotator knn =
				KNNAnnotator.create(extractor2, compar, k);

		return new EigenFaceRecogniser(extractor, knn);
	}

	@Override
	protected void beforeBatchTrain(GroupedDataset, FACE> dataset) {
		extractor.train(DatasetAdaptors.asList(dataset));
	}

	@Override
	public String toString() {
		return String.format("EigenFaceRecogniser[extractor=%s; recogniser=%s]",
				this.extractor, this.internalRecogniser);
	}
}