Low-resolution face recognition in uses of multiple-size discrete cosine transforms and selective Gaussian mixture models

Owing to losing the detailed information, the low-resolution problem in face recognition degrades the recognition performance dramatically. To overcome this problem, a novel face-recognition system has been proposed, consisting of the extracted feature vectors from the multiple-size discrete cosine transforms (mDCTs) and the recognition mechanism with selective Gaussian mixture models (sGMMs). The mDCT could extract enough visual features from low-resolution face images while the sGMM could exclude unreliable observation features in recognition phase. Thus, the mDCT and the sGMM can greatly improve recognition rate at low-resolution conditions. Experiments are carried out on George Tech and AR facial databases in 16 × 16 and 12 × 12 pixels resolution. The results show that the proposed system achieves better performance than the existing methods for low-resolution face recognition.

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

[2]  Luuk J. Spreeuwers,et al.  The Effect of Image Resolution on the Performance of a Face Recognition System , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

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

[5]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[6]  Ying-li Tian,et al.  Evaluation of Face Resolution for Expression Analysis , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[7]  Sang-Woong Lee,et al.  Low resolution face recognition based on support vector data description , 2006, Pattern Recognit..

[8]  Pong C. Yuen,et al.  Very low resolution face recognition problem , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[9]  Rama Chellappa,et al.  Special Issue on Video Analysis on Resource-Limited Systems , 2011, IEEE Trans. Circuits Syst. Video Technol..

[10]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Thomas B. Moeslund,et al.  Extracting a Good Quality Frontal Face Image From a Low-Resolution Video Sequence , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Yücel Altunbasak,et al.  Eigenface-domain super-resolution for face recognition , 2003, IEEE Trans. Image Process..

[13]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[14]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Shiguang Shan,et al.  Low-Resolution Face Recognition via Coupled Locality Preserving Mappings , 2010, IEEE Signal Processing Letters.

[16]  Alan Murray,et al.  Advances in Neural Information Processing Systems 2003 , 2003 .

[17]  Takeo Kanade,et al.  Limits on Super-Resolution and How to Break Them , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Samy Bengio,et al.  User authentication via adapted statistical models of face images , 2006, IEEE Transactions on Signal Processing.

[19]  Ning Wu,et al.  Fast Facial Image Super-Resolution via Local Linear Transformations for Resource-Limited Applications , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Zhenjiang Miao,et al.  Scale-robust feature extraction for face recognition , 2009, 2009 17th European Signal Processing Conference.

[21]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Matti Pietikäinen,et al.  From still image to video-based face recognition: an experimental analysis , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[23]  Ming-Hsuan Yang,et al.  Learning Gender with Support Faces , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Pablo H. Hennings-Yeomans,et al.  Simultaneous super-resolution and feature extraction for recognition of low-resolution faces , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Yunfei Chen,et al.  On secrecy outage of MISO SWIPT systems in the presence of imperfect CSI , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[26]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[27]  Kevin W. Bowyer,et al.  Face recognition technology: security versus privacy , 2004, IEEE Technology and Society Magazine.

[28]  B.V.K.V. Kumar,et al.  Recognition of Low-Resolution Faces Using Multiple Still Images and Multiple Cameras , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.