org.openimaj.image.processing.face.feature.FisherFaceFeature Maven / Gradle / Ivy
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/**
* 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.feature;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.AbstractList;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.data.dataset.GroupedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.FeatureVectorProvider;
import org.openimaj.image.FImage;
import org.openimaj.image.model.FisherImages;
import org.openimaj.image.processing.face.alignment.FaceAligner;
import org.openimaj.image.processing.face.detection.DetectedFace;
import org.openimaj.io.IOUtils;
import org.openimaj.ml.training.BatchTrainer;
import org.openimaj.util.pair.IndependentPair;
/**
* A {@link FacialFeature} for FisherFaces.
*
* @author Jonathon Hare ([email protected])
*/
@Reference(
type = ReferenceType.Article,
author = { "Belhumeur, Peter N.", "Hespanha, Jo\\~{a}o P.", "Kriegman, David J." },
title = "Fisherfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection",
year = "1997",
journal = "IEEE Trans. Pattern Anal. Mach. Intell.",
pages = { "711", "", "720" },
url = "http://dx.doi.org/10.1109/34.598228",
month = "July",
number = "7",
publisher = "IEEE Computer Society",
volume = "19",
customData = {
"issn", "0162-8828",
"numpages", "10",
"doi", "10.1109/34.598228",
"acmid", "261512",
"address", "Washington, DC, USA",
"keywords",
"Appearance-based vision, face recognition, illumination invariance, Fisher's linear discriminant."
})
public class FisherFaceFeature implements FacialFeature, FeatureVectorProvider {
/**
* A {@link FacialFeatureExtractor} for producing FisherFaces. Unlike most
* {@link FacialFeatureExtractor}s, this one either needs to be trained or
* provided with a pre-trained {@link FisherImages} object.
*
* A {@link FaceAligner} can be used to produce aligned faces for training
* and feature extraction.
*
* @author Jonathon Hare ([email protected])
*
* @param
*
*/
public static class Extractor
implements
FacialFeatureExtractor,
BatchTrainer>
{
FisherImages fisher = null;
FaceAligner aligner = null;
/**
* Construct with the requested number of components (the number of PCs
* to keep) and a face aligner. The principal components must be learned
* by calling {@link #train(List)}.
*
* @param numComponents
* the number of principal components to keep.
* @param aligner
* the face aligner
*/
public Extractor(int numComponents, FaceAligner aligner) {
this(new FisherImages(numComponents), aligner);
}
/**
* Construct with given pre-trained {@link FisherImages} basis and a
* face aligner.
*
* @param basis
* the pre-trained basis
* @param aligner
* the face aligner
*/
public Extractor(FisherImages basis, FaceAligner aligner) {
this.fisher = basis;
this.aligner = aligner;
}
@Override
public FisherFaceFeature extractFeature(T face) {
final FImage patch = aligner.align(face);
final DoubleFV fv = fisher.extractFeature(patch);
return new FisherFaceFeature(fv);
}
@Override
public void readBinary(DataInput in) throws IOException {
fisher.readBinary(in);
final String alignerClass = in.readUTF();
aligner = IOUtils.newInstance(alignerClass);
aligner.readBinary(in);
}
@Override
public byte[] binaryHeader() {
return this.getClass().getName().getBytes();
}
@Override
public void writeBinary(DataOutput out) throws IOException {
fisher.writeBinary(out);
out.writeUTF(aligner.getClass().getName());
aligner.writeBinary(out);
}
@Override
public void train(final List> data) {
final List> patches = new AbstractList>() {
@Override
public IndependentPair get(int index) {
return IndependentPair.pair(data.get(index).firstObject(),
aligner.align(data.get(index).secondObject()));
}
@Override
public int size() {
return data.size();
}
};
fisher.train(patches);
}
/**
* Train on a map of data.
*
* @param data
* the data
*/
public void train(Map> data) {
final List> list = new ArrayList>();
for (final Entry> e : data.entrySet()) {
for (final T i : e.getValue()) {
list.add(IndependentPair.pair(e.getKey(), aligner.align(i)));
}
}
fisher.train(list);
}
/**
* Train on a grouped dataset.
*
* @param
* The group type
* @param data
* the data
*/
public void train(GroupedDataset, T> data) {
final List> list = new ArrayList>();
for (final KEY e : data.getGroups()) {
for (final T i : data.getInstances(e)) {
if (i != null)
list.add(IndependentPair.pair(e, aligner.align(i)));
}
}
fisher.train(list);
}
}
private DoubleFV fv;
protected FisherFaceFeature() {
this(null);
}
/**
* Construct the FisherFaceFeature with the given feature vector.
*
* @param fv
* the feature vector
*/
public FisherFaceFeature(DoubleFV fv) {
this.fv = fv;
}
@Override
public void readBinary(DataInput in) throws IOException {
fv = new DoubleFV();
fv.readBinary(in);
}
@Override
public byte[] binaryHeader() {
return getClass().getName().getBytes();
}
@Override
public void writeBinary(DataOutput out) throws IOException {
fv.writeBinary(out);
}
@Override
public DoubleFV getFeatureVector() {
return fv;
}
}