On soft biometrics

Innovation has formed much of the rich history in biometrics. The field of soft biometrics was originally aimed to augment the recognition process by fusion of metrics that were sufficient to discriminate populations rather than individuals. This was later refined to use measures that could be used to discriminate individuals, especially using descriptions that can be perceived using human vision and in surveillance imagery. A further branch of this new field concerns approaches to estimate soft biometrics, either using conventional biometrics approaches or just from images alone. These three strands combine to form what is now known as soft biometrics. We survey the achievements that have been made in recognition by and in estimation of these parameters, describing how these approaches can be used and where they might lead to. The approaches lead to a new type of recognition, and one similar to Bertillonage which is one of the earliest approaches to human identification.

[1]  Els J. Kindt,et al.  Privacy and Data Protection Issues of Biometric Applications , 2013 .

[2]  Qinfen Zheng,et al.  Computational approaches for real-time extraction of soft biometrics , 2008, 2008 19th International Conference on Pattern Recognition.

[3]  Yasushi Makihara,et al.  Gait-based age estimation using a whole-generation gait database , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[4]  Kenji Sugawara,et al.  Multimodal soft biometrie verification by hand shape and handwriting motion in the air , 2013, 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013).

[5]  Anil K. Jain,et al.  Face Matching and Retrieval Using Soft Biometrics , 2010, IEEE Transactions on Information Forensics and Security.

[6]  K. Bowyer,et al.  Predicting ethnicity and gender from iris texture , 2011, 2011 IEEE International Conference on Technologies for Homeland Security (HST).

[7]  Mark S. Nixon,et al.  Imputing human descriptions in semantic biometrics , 2010, MiFor '10.

[8]  Andrea Lagorio,et al.  Distinctiveness of faces: A computational approach , 2008, TAP.

[9]  Arun Ross,et al.  Predicting gender and weight from human metrology using a copula model , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[10]  Massimo Tistarelli,et al.  Measuring changes in face appearance through aging , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Huizhong Chen,et al.  Describing Clothing by Semantic Attributes , 2012, ECCV.

[12]  Thomas B. Moeslund,et al.  Automatic Annotation of Humans in Surveillance Video , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[13]  J. Shepherd,et al.  Adult Eyewitness Testimony: Whole body information: Its relevance to eyewitnesses , 1994 .

[14]  Niels da Vitoria Lobo,et al.  Age Classification from Facial Images , 1999, Comput. Vis. Image Underst..

[15]  Julian Fiérrez,et al.  Soft Biometrics and Their Application in Person Recognition at a Distance , 2014, IEEE Transactions on Information Forensics and Security.

[16]  Shaogang Gong,et al.  Person Re-identification by Attributes , 2012, BMVC.

[17]  Arun Ross,et al.  What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics , 2016, IEEE Transactions on Information Forensics and Security.

[18]  Guodong Guo,et al.  Gender from Body: A Biologically-Inspired Approach with Manifold Learning , 2009, ACCV.

[19]  J. Gregory Trafton,et al.  Complexion as a soft biometric in human-robot interaction , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[20]  Jian Dong,et al.  Deep domain adaptation for describing people based on fine-grained clothing attributes , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Mark S. Nixon,et al.  Performing content-based retrieval of humans using gait biometrics , 2008, Multimedia Tools and Applications.

[22]  Patrick J. Flynn,et al.  The prediction of old and young subjects from iris texture , 2013, 2013 International Conference on Biometrics (ICB).

[23]  Shumeet Baluja,et al.  Boosting Sex Identification Performance , 2005, International Journal of Computer Vision.

[24]  Yun Fu,et al.  Human age estimation using bio-inspired features , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Jiajia Lei,et al.  Gender classification using automatically detected and aligned 3D ear range data , 2013, 2013 International Conference on Biometrics (ICB).

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

[27]  Jean-Luc Dugelay,et al.  Bag of soft biometrics for person identification , 2010, Multimedia Tools and Applications.

[28]  Salman Yussof,et al.  Recent advances in facial soft biometrics , 2014, The Visual Computer.

[29]  Nicole A. Spaun Forensic Biometrics from Images and Video at the Federal Bureau of Investigation , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

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

[31]  Venu Govindaraju,et al.  Facial behavior as a soft biometric , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[32]  Remo Sala,et al.  New method for height estimation of subjects represented in photograms taken from video surveillance systems , 2007, International Journal of Legal Medicine.

[33]  Jean-Luc Dugelay,et al.  On the reliability of eye color as a soft biometric trait , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[34]  Sridha Sridharan,et al.  Identifying Customer Behaviour and Dwell Time Using Soft Biometrics , 2012, Video Analytics for Business Intelligence.

[35]  Shrikanth S. Narayanan,et al.  Automatic speaker age and gender recognition using acoustic and prosodic level information fusion , 2013, Comput. Speech Lang..

[36]  Michael Fairhurst,et al.  Age Factors in Biometric Processing , 2013 .

[37]  Simon A. Cole,et al.  Twins, Twain, Galton, and Gilman: Fingerprinting, Individualization, Brotherhood, and Race in Pudd’nhead Wilson , 2009 .

[38]  Anil K. Jain,et al.  Age , Gender and Race Estimation from Unconstrained Face Images , 2014 .

[39]  Anil K. Jain,et al.  Soft Biometric Traits for Personal Recognition Systems , 2004, ICBA.

[40]  Mark S. Nixon,et al.  Soft Biometrics; Human Identification Using Comparative Descriptions , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Mark S. Nixon,et al.  Soft biometrics for subject identification using clothing attributes , 2014, IEEE International Joint Conference on Biometrics.

[42]  Bruce A. Draper,et al.  On the effectiveness of soft biometrics for increasing face verification rates , 2015, Comput. Vis. Image Underst..

[43]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[44]  Mark S. Nixon,et al.  Viewpoint invariant subject retrieval via soft clothing biometrics , 2015, 2015 International Conference on Biometrics (ICB).

[45]  K.W. Bowyer,et al.  Learning to predict gender from iris images , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

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

[47]  Matti Pietikäinen,et al.  Demographic classification from face videos using manifold learning , 2013, Neurocomputing.

[48]  Shuicheng Yan,et al.  Clothing Attributes Assisted Person Reidentification , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[49]  Maciej Henneberg,et al.  Comparing the face to the body, which is better for identification? , 2016, International Journal of Legal Medicine.

[50]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.

[51]  Bok-Min Goi,et al.  Recognizing Human Gender in Computer Vision: A Survey , 2012, PRICAI.

[52]  Arun Ross,et al.  Predictability and correlation in human metrology , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[53]  Mark S. Nixon,et al.  Human identification using facial comparative descriptions , 2013, 2013 International Conference on Biometrics (ICB).

[54]  J. Dugelay,et al.  Demographic classification: Do gender and ethnicity affect each other? , 2012, 2012 International Conference on Informatics, Electronics & Vision (ICIEV).

[55]  E. Mordini,et al.  Body, Biometrics and Identity , 2008, Bioethics.

[56]  Antonio Criminisi,et al.  New approach to obtain height measurements from video , 1999, Other Conferences.

[57]  Julian Fiérrez,et al.  Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics , 2014, ECCV Workshops.

[58]  Andreas Lanitis,et al.  An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging Database , 2014, ECCV Workshops.

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

[60]  Sridha Sridharan,et al.  Soft-Biometrics: Unconstrained Authentication in a Surveillance Environment , 2009, 2009 Digital Image Computing: Techniques and Applications.

[61]  Mark S. Nixon,et al.  On a Large Sequence-Based Human Gait Database , 2004 .

[62]  Andrew C. Gallagher,et al.  Understanding images of groups of people , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[63]  Arun Ross,et al.  Evaluation of Texture Descriptors for Automated Gender Estimation from Fingerprints , 2014, ECCV Workshops.

[64]  Larry S. Davis,et al.  Person identification using automatic height and stride estimation , 2002, Object recognition supported by user interaction for service robots.

[65]  Haibo He,et al.  Learning Race from Face: A Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  Roope Raisamo,et al.  An experimental comparison of gender classification methods , 2008, Pattern Recognit. Lett..

[67]  Arun Ross,et al.  Periocular Biometrics in the Visible Spectrum , 2011, IEEE Transactions on Information Forensics and Security.

[68]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[69]  Duncan S. Wong,et al.  Touch Gestures Based Biometric Authentication Scheme for Touchscreen Mobile Phones , 2012, Inscrypt.

[70]  Michael C. Fairhurst,et al.  Improved age prediction from biometric data using multimodal configurations , 2014, 2014 International Conference of the Biometrics Special Interest Group (BIOSIG).

[71]  Jean-Luc Dugelay,et al.  Color based soft biometry for hooligans detection , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[72]  Nicolas Tsapatsoulis,et al.  Quantitative evaluation of the effects of aging on biometric templates , 2011 .

[73]  Jean-Luc Dugelay,et al.  Weight estimation from visual body appearance , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[74]  M.S. Nixon,et al.  The Use of Semantic Human Description as a Soft Biometric , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[75]  Hugo Van hamme,et al.  Speaker age estimation and gender detection based on supervised Non-Negative Matrix Factorization , 2011, 2011 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS).

[76]  Anil K. Jain,et al.  Soft Biometrics , 2009, Encyclopedia of Biometrics.

[77]  Jiwen Lu,et al.  Gait-Based Human Age Estimation , 2010, IEEE Transactions on Information Forensics and Security.

[78]  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).

[79]  Mark S. Nixon,et al.  Gender Classification in Human Gait Using Support Vector Machine , 2005, ACIVS.

[80]  Christophe Rosenberger,et al.  Soft biometrics database: A benchmark for keystroke dynamics biometric systems , 2013, 2013 International Conference of the BIOSIG Special Interest Group (BIOSIG).

[81]  Terrence J. Sejnowski,et al.  SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.

[82]  Jean-Luc Dugelay,et al.  Person recognition using a bag of facial soft biometrics (BoFSB) , 2010, 2010 IEEE International Workshop on Multimedia Signal Processing.

[83]  Paul C. Miller,et al.  Full body image feature representations for gender profiling , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[84]  Iztok Fister,et al.  A biometric authentication model using hand gesture images , 2013, BioMedical Engineering OnLine.

[85]  Domingo Mery,et al.  Automatic facial attribute analysis via adaptive sparse representation of random patches , 2015, Pattern Recognit. Lett..

[86]  Arun Ross,et al.  Impact of facial cosmetics on automatic gender and age estimation algorithms , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[87]  Mark S. Nixon,et al.  On Semantic Soft-Biometric Labels , 2014, BIOMET.

[88]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[89]  Marina L. Gavrilova,et al.  A Concept of Social Behavioral Biometrics: Motivation, Current Developments, and Future Trends , 2014, 2014 International Conference on Cyberworlds.

[90]  Daniel Martinho-Corbishley,et al.  Soft biometric recognition from comparative crowdsourced annotations , 2015, ICDP.

[91]  Janusz Konrad,et al.  Towards Gesture-Based User Authentication , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[92]  Arun Ross,et al.  Soft biometrics for surveillance: an overview , 2013 .

[93]  Marios Savvides,et al.  An exploration of gender identification using only the periocular region , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[94]  Damon L. Woodard,et al.  Soft biometric classification using local appearance periocular region features , 2012, Pattern Recognit..

[95]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[96]  Thomas B. Moeslund,et al.  Multimodal person re-identification using RGB-D sensors and a transient identification database , 2013, 2013 International Workshop on Biometrics and Forensics (IWBF).

[97]  Mark S. Nixon,et al.  Using comparative human descriptions for soft biometrics , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[98]  A.K. Jain,et al.  Scars, marks and tattoos (SMT): Soft biometric for suspect and victim identification , 2008, 2008 Biometrics Symposium.

[99]  Jiwen Lu,et al.  Body-based human age estimation at a distance , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[100]  Anil K. Jain,et al.  Suspect identification based on descriptive facial attributes , 2014, IEEE International Joint Conference on Biometrics.

[101]  Anil K. Jain,et al.  Can soft biometric traits assist user recognition? , 2004, SPIE Defense + Commercial Sensing.

[102]  Anil K. Jain,et al.  Periocular biometrics in the visible spectrum: A feasibility study , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.