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Implementation of a flexible face-recognition pipeline,
including pluggable detectors, aligners, feature extractors
and recognisers.
/**
* 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 java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.Collection;
import java.util.List;
import java.util.Set;
import org.openimaj.data.dataset.GroupedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.data.dataset.cache.GroupedListCache;
import org.openimaj.data.dataset.cache.InMemoryGroupedListCache;
import org.openimaj.feature.FeatureExtractor;
import org.openimaj.image.processing.face.detection.DetectedFace;
import org.openimaj.io.IOUtils;
import org.openimaj.ml.annotation.Annotated;
import org.openimaj.ml.annotation.ScoredAnnotation;
/**
* A face recogniser that caches detected faces and only performs actual
* training when required. Provided as a base for the eigen and fisher face
* recognisers as they typically need to train the feature extractors before
* use.
*
* @author Jonathon Hare ([email protected])
*
* @param
* Type of {@link DetectedFace}
* @param
* Type of object representing a person
*/
abstract class LazyFaceRecogniser>
extends
FaceRecogniser
{
EXTRACTOR extractor;
FaceRecogniser internalRecogniser;
GroupedListCache faceCache;
boolean isInvalid = true;
protected LazyFaceRecogniser() {
}
/**
* Construct with an in-memory cache and the given internal face recogniser.
* It is assumed that the internals of the given recogniser are somehow
* linked to or use the given feature extractor.
*
* @param extractor
* the feature extractor
* @param internalRecogniser
* the internal recogniser.
*/
public LazyFaceRecogniser(EXTRACTOR extractor, FaceRecogniser internalRecogniser)
{
this.extractor = extractor;
this.internalRecogniser = internalRecogniser;
faceCache = new InMemoryGroupedListCache();
}
@Override
public void readBinary(DataInput in) throws IOException {
final LazyFaceRecogniser wrapper = IOUtils.read(in);
this.extractor = wrapper.extractor;
this.faceCache = wrapper.faceCache;
this.internalRecogniser = wrapper.internalRecogniser;
this.isInvalid = wrapper.isInvalid;
}
@Override
public void writeBinary(DataOutput out) throws IOException {
IOUtils.write(this, out);
}
@Override
public byte[] binaryHeader() {
return "BFRec".getBytes();
}
@Override
public void train(Annotated annotated) {
faceCache.add(annotated.getAnnotations(), annotated.getObject());
isInvalid = true;
}
@Override
public void reset() {
internalRecogniser.reset();
faceCache.reset();
isInvalid = true;
}
@Override
public Set getAnnotations() {
return faceCache.getDataset().getGroups();
}
/**
* Called before batch training/re-training takes place.
*
* @param dataset
* the dataset
*/
protected abstract void beforeBatchTrain(GroupedDataset, FACE> dataset);
private void retrain() {
if (isInvalid) {
final GroupedDataset, FACE> dataset = faceCache.getDataset();
beforeBatchTrain(dataset);
internalRecogniser.train(dataset);
isInvalid = false;
}
}
@Override
public List> annotate(FACE object, Collection restrict) {
retrain();
return internalRecogniser.annotate(object, restrict);
}
@Override
public List> annotate(FACE object) {
retrain();
return internalRecogniser.annotate(object);
}
}