Fast and Efficient Face Image Browsing System on Consumer Electronics Devices

In this paper, we develop a fast and efficient face image browsing system on CE (Consumer Electronics) devices. Our system adopts three methods such as facial region de- tection and facial feature extraction, facial vector cluster- ing, and DB handling for face metadata. Given photos, a facial region detection algorithm is applied to each photo and then Gabor-Wavelet features of fiducial points in each detected face are extracted. Next, a facial vector clustering algorithm makes all the facial features, which corresponds to faces, be clustered in an appropriate manner. In general, a face recognition algorithm requires a registration proce- dure, i.e., a user should register face images as the gallery, but our system clusters daily photos automatically. Finally, the DB is updated in order to browse photos by face meta- data in realtime. This paper presents the developed algo- rithms of the three methods in detail and an easy and effi- cient user interface for a face image browsing system on CE devices with its snapshots.

[1]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[2]  Mohamed Rizon,et al.  Detection of eyes from human faces by Hough transform and separability filter , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[5]  Yuandong Tian,et al.  EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking , 2007, CHI.

[6]  Dragomir Anguelov,et al.  Contextual Identity Recognition in Personal Photo Albums , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[9]  Narendra Ahuja,et al.  A SNoW-Based Face Detector , 1999, NIPS.

[10]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[11]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[12]  Bernhard Fröba,et al.  Robust Face Detection at Video Frame Rate Based on Edge Orientation Features , 2002, FGR.

[13]  Tsuhan Chen,et al.  Using Group Prior to Identify People in Consumer Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  TaeWoong Jung,et al.  Towards Multimedia Contents Management System on Consumer Electronics Devices , 2006, Eighth IEEE International Symposium on Multimedia (ISM'06).

[15]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.