A Colour Face Image Database for Benchmarking of Automatic Face Detection Algorithms

1. A Colour Face Image Database for Benchmarking of Automatic Face Detection Algorithms Prag Sharma, Richard B. Reilly UCD DSP Research Group This work is supported by Enterprise Ireland under the Informatics Research Initiative

2. Aim • To develop a Colour Image Database that can be used as a standard database for testing and evaluating Face Detection Algorithms. • To make this database available to the research community. DSP Research Group, University College Dublin

3. Face Detection: Applications and Challenges Posed DSP Research Group, University College Dublin

4. Need for Face Detection • Face Recognition • Most face recognition algorithms assume that the face is already located. • Not so in video surveillance and interactive multimedia applications. • Intelligent Vision-based Human Computer Interaction • Expression recognition. • Use of computers by diabled people. DSP Research Group, University College Dublin

5. Need for Face Detection • Object-based Video Processing • Scene composed of ‘Objects’ rather than by pixels or block of pixels. (MPEG4) • Content-based functionalities: Objects multiplexed seperately so that the receiver can manipulate each object independently. • Improved Coding Efficiency: Choosing the best coding strategy for the face region which might be of greater interest than the background. • Improved Error-Robustness • Content Description: Description of scene much more efficient, allowing faster access and retrevial of the desired information. (MPEG7) DSP Research Group, University College Dublin

6. Challenges Associated with Face Detection • Pose Estimation and Orientation • Faces vary due to relative camera-face pose and orientation. e.g. Frontal, 45º, Profile, Upside Down. • Presence or Absence of Structural Components • Facial features such as beards, moustaches and glasses may or may not be present. DSP Research Group, University College Dublin

7. Challenges Associated with Face Detection • Facial Expressions and Occlusion • Imaging Conditions • Lighting and camera characteristics directly affect the appearance of a face. DSP Research Group, University College Dublin

8. Existing Face Image Databases DSP Research Group, University College Dublin

9. Existing Face Image Databases • Databases for Face Recognition • Contain face images taken with a specific set-up in order to maintain a degree of consistency. • FERET Database: Grayscale images with head and neck visible only on a uniform and uncluttered background in frontal position. • MIT Database: Frontal and near-frontal images on a cluttered background. DSP Research Group, University College Dublin

10. Existing Face Image Databases • Face Recognition Databases do not provide the challenges encountered by Face Detection algorithms. • Poor lighting conditions. • Poor Quality Images. • Presence of multiple faces. • Some databases exist that specifically cater for face detection problems. • However, most of these databases have grayscale images only!!!! DSP Research Group, University College Dublin

11. Existing Face Image Databases DSP Research Group, University College Dublin

12. Existing Face Image Databases MIT Face Image Database DSP Research Group, University College Dublin

13. Existing Face Image Databases CMU Face Image Database DSP Research Group, University College Dublin

14. Need for a New Database • New Face Detection approaches that use multiple features such as skin colour, shape, size and presence of facial feature are being developed. • A typical approach starts with skin-colour based region segmentation. DSP Research Group, University College Dublin

15. Need for a New Database • Skin detection has some significant advantages. • Processing of colour information has proven to be much faster than the processing of other facial characteristics. • An effective colour model can adapt to varying lighting conditions. • Colour is invariant to change in shape, size, orientation and partial occlusion of the face. • However, colour-based face detection algorithms often use very different test sets!!!! • Need for a standard database that can be used objectively by all existing algorithms. DSP Research Group, University College Dublin

16. The UCD Colour Face Image Database • The database has two parts. • Part I contains a 100 colour images of faces with variations in the following: • Background: Indoor, Outdoors, Cluttered, Uncluttered. • Facial Structural Components: Beard, Moustaches and Glasses. • Poses: Frontal, Near-Frontal and Profile. • Orientation: Upright and Rotated. • Imaging Conditions: Poor Quality, Good Quality and Variable Lighting. • Variability in facial expressions, occlusian, age, gender, race and size. DSP Research Group, University College Dublin

17. The UCD Colour Face Image Database • The images have been captured from the following sources: • Digital Cameras. • Pictures scanned in using a scanner. • Images from the World Wide Web. • Images from existing face recognition and detection databases. • All images are original images without any pre-processing. • No restriction on face size or image quality is imposed. DSP Research Group, University College Dublin

18. The UCD Colour Face Image Database • Details of the UCD Database are shown below: Note: Intermediate: A face pose that is neither frontal nor profile. Upright:A face is considered upright if the major axis of the best-fit ellipse makes an angle of less than 150 with the vertical axis. DSP Research Group, University College Dublin

19. The UCD Colour Face Image Database • Details of the UCD Database are shown below: • A spreadsheet with details of the faces present in each image of the database is also provided. DSP Research Group, University College Dublin

20. The UCD Colour Face Image Database • Some images from the database are shown below. DSP Research Group, University College Dublin

21. The UCD Colour Face Image Database • The database has two parts. • Part II of the database comes with hand segmented results of each image in Part I. • Thus, automatic performance evaluation can be performed by comparing the location of detected regions with hand segmented results and using an accuracy measure to confirm a correctly detected face. DSP Research Group, University College Dublin

22. Conclusions • A standard Colour Face Image Database is proposed for evaluation of face detection algorithms. • The database contains faces with a high degree of variability to challenge all existing face detection algorithms. • The database also provides details of each image in an excel file together with hand segmented results for automatic performance evaluation. • The database is available to the academic community by contacting the author at prag@ee.ucd.ieor visiting the website at http://dsp.ucd.ie/~prag DSP Research Group, University College Dublin

23. Questions Please note that since the author is not present all questions will be forwarded to the author or you can contact the author at a later stage at prag@ee.ucd.ie DSP Research Group, University College Dublin