challenge.uccs 1.0.0

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. Hence, there are lots of misdetections (false negatives) and detected background areas (false positives).

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

--which-set: The set, for which the baseline detector should be run; validation by default

--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

By default, the face detection score file will be written to ./results/UCCS-detection-baseline.txt.

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 crops, 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

By default, the face recognition score file will be written to ./results/UCCS-recognition-baseline.txt.

Face Detection

As the ground-truth is usually larger than the face, we do not punish bounding boxes that are smaller than the ground truth. Therefore, the union (the denominator) takes into account only one fourth of the ground truth boudning box – or the intersection area, whichever is larger:

where \(|\cdot|\) is the area operator. Hence, when the detected bounding box \(D\) covers at least a fourth of the ground-truth bounding box \(G\) and is entirely contained inside \(G\), an overlap of 1 is reached.

The face detection is evaluated using the Free Receiver Operator Characteristic (FROC) curve, which plots the percentage of correctly detected faces over the total number of false positives (false alarms). This plot can be created using:

$ ./bin/evaluate_detector.py

This script has several options, some of which need to be specified, see ./bin/evaluate_detector.py -h:

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

--result-files: A list of all files containing detection (or recognition) results

--labels: A list of labels for the algorithms; must be the same number and in the same order as --result-files

--froc-file: The name of the output .pdf file containing the FROC plot

--log-x: will plot the horizontal axis in logarithmic scale

--only-present: will ignore any file for which no detection exists (for debug purposes only, i.e., when detector ran with the --debug option)

--verbose: Increase the verbosity of the script; using --verbose --verbose or -vv is recommended

To plot the baseline FROC curve (which is shown on the competition website), execute:

$ ./bin/evaluate_detector.py –data-directory YOUR-DATA-DIRECTORY –result-files results/UCCS-detection-baseline.txt –labels Baseline -vv

Face Recognition

Open set face recognition is evaluated using the Detection and Identification Rate (DIR) curve, which plots the percentage of correctly detected and identified faces over the false alarm rate (FAR). Based on various values of the FAR, several score thresholds are computed. A face is said to be identified correctly if the recognition score is greater than the threshold and the correct identity has the highest recognition score for that face. The number of correctly identified faces is computed, and divided by the total number of recognition scores greater than the threshold. For more details, please refer to [1].

The DIR plot can be created using:

$ ./bin/evaluate_recognizer.py

As usual, the script has several options, which are similar to ./bin/evaluate_detector.py above, see ./bin/evaluate_recognizer.py -h for a complete list:

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

--result-files: A list of all files containing recognition results

--labels: A list of labels for the algorithms; must be the same number and in the same order as --result-files

--dir-file: The name of the output .pdf file containing the DIR plot

--log-x: will plot the horizontal axis in logarithmic scale

--only-present: will ignore any file for which no detection exists (for debug purposes only, i.e., when recognizer ran with the --debug option)

--verbose: Increase the verbosity of the script; using --verbose --verbose or -vv is recommended

--rank: Use the given rank to plot the DIR curve

To plot the baseline Rank 1 DIR curve (which is shown on the competition website), execute:

$ ./bin/evaluate_recognizer.py –data-directory YOUR-DATA-DIRECTORY –result-files results/UCCS-recognition-baseline.txt –labels Baseline -vv