Soft-Biometric Attributes from Selfie Images

The aim of this chapter is to discuss the soft-biometric attributes that can be extracted from selfie images acquired from mobile devices. Existing literature suggests that various features in demographics, such as gender and age, in physical, such as periocular and eyebrow, and in material, such as eyeglasses and clothing, have been extracted from selfie images for continuous user authentication and performance enhancement of primary biometric traits. Due to the limited hardware resources, low resolution of front-facing cameras, and the usage of the device in different environmental conditions, factors such as robustness to low-quality data, consent-free acquisition, lower computational complexity, and privacy, favor soft-biometric prediction in mobile devices.

[1]  Lev Manovich,et al.  Selfiecity: Exploring Photography and Self-Fashioning in Social Media , 2015 .

[2]  Ajita Rattani,et al.  Ocular biometrics in the visible spectrum: A survey , 2017, Image Vis. Comput..

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

[4]  Rama Chellappa,et al.  Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges , 2016, IEEE Signal Processing Magazine.

[5]  Sébastien Marcel,et al.  Periocular biometrics in mobile environment , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[6]  Henry T. F. b. Rhodes,et al.  Alphonse Bertillon, Father of Scientific Detection , 2013 .

[7]  Muhammad Hussain,et al.  ConvSRC: SmartPhone based Periocular Recognition using Deep Convolutional Neural Network and Sparsity Augmented Collaborative Representation , 2018, J. Intell. Fuzzy Syst..

[8]  Thomas S. Huang,et al.  Age Synthesis and Estimation via Faces , 2013 .

[9]  Vivek Kanhangad,et al.  Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[10]  Yun Fu,et al.  Human Age Estimation With Regression on Discriminative Aging Manifold , 2008, IEEE Transactions on Multimedia.

[11]  Ajita Rattani,et al.  Convolutional neural networks for gender prediction from smartphone-based ocular images , 2018, IET Biom..

[12]  Michael Fairhurst,et al.  Age prediction from iris biometrics , 2013, ICDP.

[13]  Tal Hassner,et al.  Age and gender classification using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[14]  Muhammad Khan,et al.  Iris-pupil thickness based method for determining age group of a person , 2016, Int. Arab J. Inf. Technol..

[15]  Ajita Rattani,et al.  Convolutional neural network for age classification from smart-phone based ocular images , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[16]  Tal Hassner,et al.  Effective face frontalization in unconstrained images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Rama Chellappa,et al.  Convolutional neural networks for attribute-based active authentication on mobile devices , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

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

[19]  B. Musil,et al.  What Is Seen Is Who You Are: Are Cues in Selfie Pictures Related to Personality Characteristics? , 2017, Front. Psychol..

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

[21]  Reza Derakhshani,et al.  Gender prediction from mobile ocular images: A feasibility study , 2017, 2017 IEEE International Symposium on Technologies for Homeland Security (HST).

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

[23]  Ahmad Saeed Mohammad,et al.  Eyeglasses detection based on learning and non-learning based classification schemes , 2017, 2017 IEEE International Symposium on Technologies for Homeland Security (HST).

[24]  Margit Antal,et al.  Gender recognition from mobile biometric data , 2016, 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI).

[25]  Tieniu Tan,et al.  Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices , 2018, IEEE Transactions on Information Forensics and Security.

[26]  Zhu Li,et al.  User Re-Identification Using Clothing Information for Smartphones , 2018, 2018 IEEE International Symposium on Technologies for Homeland Security (HST).

[27]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[28]  Ajita Rattani,et al.  Short-Term User Authentication Using Eyebrows Biometric For Smartphone Devices , 2018, 2018 10th Computer Science and Electronic Engineering (CEEC).

[29]  Cuixian Chen,et al.  Face age estimation using model selection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[30]  Lev Manovich,et al.  Zooming into an Instagram City: Reading the local through social media , 2013, First Monday.

[31]  Terrance E. Boult,et al.  Multi-attribute spaces: Calibration for attribute fusion and similarity search , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Arun Ross,et al.  On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery , 2010, 2010 20th International Conference on Pattern Recognition.

[33]  Rama Chellappa,et al.  Attribute-based continuous user authentication on mobile devices , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).