Face Detection

The first script is a face detection script, which will detect the faces in the validation (and test) set. The baseline face detector simply uses Bob’s built-in face detector bob.ip.facedetect, which is neither optimized for blurry faces nor for profiles.

You can call the face detector baseline script using:

$ ./bin/baseline_detector.py

Please refer to $ ./bin/baseline_detector.py -h for possible options. Here is a subset of options that you might want/need to use/change:

--data-directory: Specify the directory, where you have downloaded the UCCS dataset into

--result-file: The file to write the detection results into; this will be in the required format

--verbose: Increase the verbosity of the script; using --verbose --verbose or -vv is recommended; -vvv will write more information

--debug: Run only over the specified number of images; for debug purposes only

--display: Display the detected bounding boxes and the ground truth; for debug purposes only

--parallel: Run in the given number of parallel processes; can speed up the processing tremendously

On a machine with 32 cores, a good command line for the full baseline experiment would be:

$ ./bin/baseline_detector.py –data-directory YOUR-DATA-DIRECTORY -vv –parallel 32

To run a small-scale experiment, i.e., to display the detected faces on 20 images, a good command line might be:

$ ./bin/baseline_detector.py –data-directory YOUR-DATA-DIRECTORY -vvv –display –debug 20

Face Recognition

For face recognition, we simply adopt a PCA+LDA pipeline on top of LBPHS features. The PCA+LDA projection matrix is estimated from the faces in the training set. For each person, the images of the training set build one class. Open-set recognition is performed by using all training faces of unknown identities in a separate class.

First, the faces in the training images are re-detected, to assure that the bounding boxes of training and test images have similar content. Then, the faces are rescaled and cropped to a resolution of 64x80 pixels. Afterwards, LPBHS features are extracted from these images, and a PCA+LDA projection matrix is computed. All training features are projected into the PCA+LDA subspace. For each identity (including the unknown identity -1), the average of the projected features is stored as a template.

During testing, in each image all faces are detected, cropped, and LBPHS features are extracted. Those probe features are projected into the same PCA+LDA subspace, and compared to all templates using Euclidean distance. For each detected face, the 10 identities with the smallest distances are obtained – if identity -1 is included, all less similar images are not considered anymore. These scores are written into the score file in the desired format.

You can call the face recognition baseline script using:

$ ./bin/baseline_recognizer.py

Please refer to $ ./bin/baseline_recognizer.py -h for possible options. Here is a subset of options that you might want/need to use/change:

--data-directory: Specify the directory, where you have downloaded the UCCS dataset into

--result-file: The file to write the recognition results into; this will be in the required format

--verbose: Increase the verbosity of the script; using --verbose --verbose or -vv is recommended; -vvv will write more information

--temp-dir: Specify the directory, where temporary files are stored; these files will be computed only once and reloaded if present

--force: Ignore existing temporary files and always recompute everything

--debug: Run only over the specified number of identities; for debug purposes only; will modify file names of temporary files and result file

--display: Display the detected bounding boxes and the ground truth; for debug purposes only

--parallel: Run in the given number of parallel processes; can speed up the processing tremendously

On a machine with 32 cores, a good command line would be:

$ ./bin/baseline_recognizer.py –data-directory YOUR-DATA-DIRECTORY -vv –parallel 32