Personalized Age Progression with Aging Dictionary

In this paper, we aim to automatically render aging faces in a personalized way. Basically, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each subject may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups. Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.

[1]  D. Perrett,et al.  Perception of age in adult Caucasian male faces: computer graphic manipulation of shape and colour information , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[2]  Bernard Tiddeman,et al.  Prototyping and Transforming Facial Textures for Perception Research , 2001, IEEE Computer Graphics and Applications.

[3]  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.

[4]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Maulin R. Gandhi A Method for Automatic Synthesis of Aged Human Facial Images , 2004 .

[6]  Rama Chellappa,et al.  Face Verification Across Age Progression , 2006, IEEE Transactions on Image Processing.

[7]  Christopher J. Solomon,et al.  A person-specific, rigorous aging model of the human face , 2006, Pattern Recognit. Lett..

[8]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[9]  Xinggang Lin,et al.  Age simulation for face recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[10]  Francisco J. Perales López,et al.  A Facial Aging Simulation Method Using flaccidity deformation criteria , 2006, Tenth International Conference on Information Visualisation (IV'06).

[11]  Greyce N. Schroeder,et al.  Facial Aging Using Image Warping , 2007 .

[12]  A. Albert,et al.  A review of the literature on the aging adult skull and face: implications for forensic science research and applications. , 2007, Forensic science international.

[13]  K. Ricanek,et al.  Comparison of synthetic face aging to age progression by forensic sketch artist , 2007 .

[14]  Hans-Peter Seidel,et al.  Prediction of Individual Non‐Linear Aging Trajectories of Faces , 2007, Comput. Graph. Forum.

[15]  Yixiong Liang,et al.  Age Simulation in Young Face Images , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[16]  Yiying Tong,et al.  Face recognition with temporal invariance: A 3D aging model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[17]  R. Chellappa,et al.  Age progression in Human Faces : A Survey , 2008 .

[18]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[19]  Min Chen,et al.  Image‐based Aging Using Evolutionary Computing , 2008, Comput. Graph. Forum.

[20]  David W. Hunter,et al.  Synthesis of facial ageing transforms using three-dimensional morphable models , 2009 .

[21]  Rama Chellappa,et al.  Computational methods for modeling facial aging: A survey , 2009, J. Vis. Lang. Comput..

[22]  Yun Fu,et al.  Age Synthesis and Estimation via Faces: A Survey , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Shiguang Shan,et al.  A Compositional and Dynamic Model for Face Aging , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Pascal Paysan,et al.  Statistical modeling of facial aging based on 3D scans , 2010 .

[25]  Karl Ricanek,et al.  A hierarchical approach to facial aging , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[26]  Yiying Tong,et al.  Age-Invariant Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Yixiong Liang,et al.  A Multi-layer Model for Face Aging Simulation , 2011, Trans. Edutainment.

[28]  Sheng-Wen Shih,et al.  Exemplar-based Age Progression Prediction in Children Faces , 2011, 2011 IEEE International Symposium on Multimedia.

[29]  Ira Kemelmacher-Shlizerman,et al.  Collection flow , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Rama Chellappa,et al.  Age Estimation and Face Verification Across Aging Using Landmarks , 2012, IEEE Transactions on Information Forensics and Security.

[31]  Shigeo Morishima,et al.  Facial aging simulator considering geometry and patch-tiled texture , 2012, SIGGRAPH '12.

[32]  Wen Gao,et al.  A Concatenational Graph Evolution Aging Model , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Dahua Lin,et al.  Hidden Factor Analysis for Age Invariant Face Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[34]  Richa Singh,et al.  Bacteria Foraging Fusion for Face Recognition across Age Progression , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[35]  Ira Kemelmacher-Shlizerman,et al.  Illumination-Aware Age Progression , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[37]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Chu-Song Chen,et al.  Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval , 2014, ECCV.

[39]  Shiguang Shan,et al.  Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).