/**
* Find the most likely person based on a detection. Returns the index, and stores the confidence
* value into pConfidence.
*
* @param projectedTestFace the projected test face
* @param pConfidencePointer a pointer containing the confidence value
* @param iTestFace the test face index
* @return the index
*/
private int findNearestNeighbor(float projectedTestFace[], FloatPointer pConfidencePointer) {
double leastDistSq = Double.MAX_VALUE;
int i = 0;
int iTrain = 0;
int iNearest = 0;
LOGGER.info("................");
LOGGER.info("find nearest neighbor from " + nTrainFaces + " training faces");
for (iTrain = 0; iTrain < nTrainFaces; iTrain++) {
// LOGGER.info("considering training face " + (iTrain + 1));
double distSq = 0;
for (i = 0; i < nEigens; i++) {
// LOGGER.debug(" projected test face distance from eigenface " + (i + 1) + " is " +
// projectedTestFace[i]);
float projectedTrainFaceDistance = (float) projectedTrainFaceMat.get(iTrain, i);
float d_i = projectedTestFace[i] - projectedTrainFaceDistance;
distSq +=
d_i
* d_i; // / eigenValMat.data_fl().get(i); // Mahalanobis distance (might give
// better results than Eucalidean distance)
// if (iTrain < 5) {
// LOGGER.info(" ** projected training face " + (iTrain + 1) + " distance from
// eigenface " + (i + 1) + " is " + projectedTrainFaceDistance);
// LOGGER.info(" distance between them " + d_i);
// LOGGER.info(" distance squared " + distSq);
// }
}
if (distSq < leastDistSq) {
leastDistSq = distSq;
iNearest = iTrain;
LOGGER.info(
" training face "
+ (iTrain + 1)
+ " is the new best match, least squared distance: "
+ leastDistSq);
}
}
// Return the confidence level based on the Euclidean distance,
// so that similar images should give a confidence between 0.5 to 1.0,
// and very different images should give a confidence between 0.0 to 0.5.
float pConfidence =
(float) (1.0f - Math.sqrt(leastDistSq / (float) (nTrainFaces * nEigens)) / 255.0f);
pConfidencePointer.put(pConfidence);
LOGGER.info(
"training face " + (iNearest + 1) + " is the final best match, confidence " + pConfidence);
return iNearest;
}
