Abstract
Taking a ‘selfie’ using a mobile device has become a natural gesture in everyday life. This simple action has many similarities to face authentication on a smartphone: positioning the camera, adjusting the pose, choosing the right background and looking for the best lighting conditions. In the context of face authentication, most of the standardised processes and best practice for image quality is mainly focused on passport images and only recently has the attention of research moved to mobile devices. There is a lack of an agile methodology that adapts the characteristics of facial images taken on smartphone cameras in an unconstrained environment. The main objective of our study is to improve the performances of facial verification systems when implemented on smartphones. We asked 53 participants to take a minimum of 150 ‘selfies’ suitable for biometric verification on an Android smartphone. Images were considered from constrained and unconstrained environments, where users took images both in indoor and outdoor locations, simulating real-life scenarios. We subsequently calculated the quality metrics for each image. To understand how each quality metric affected the authentication outcome, we obtained biometric scores from the comparison of each image to a range of images. Our results describe how each quality metric is affected by the environment variations and user pose using the biometric scores obtained. Our study is a contribution to improve the performance and the adaptability of face verification systems to any environmental conditions, applications and devices.
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References
Bonneau J (2012) The science of guessing: analyzing an anonymized corpus of 70 million passwords. In: 2012 IEEE symposium on security and privacy (SP), pp 538–552
Uellenbeck S, Dürmuth M, Wolf C, Holz T (2013) Quantifying the security of graphical passwords: the case of android unlock patterns. In: Proceedings of the 2013 ACM SIGSAC conference on computer & communications security, pp 161–172
Android (2018) Smart lock [Online]. Available: https://get.google.com/smartlock/
Apple Inc. (2018) About face ID advanced technology [Online]. Available: https://support.apple.com/en-gb/HT208108
De Luca, A, Hang A, Von Zezschwitz E, Hussmann H (2015) I feel like I’m taking selfies all day!: towards understanding biometric authentication on smartphones. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp 1411–1414
Cook S (2017) Selfie banking: is it a reality? Biomet Technol Today 3:9–11
ISO/IEC 19794-5:2011—Information technology—biometric data interchange formats—part 5: face image data [Online]. Available: http://www.iso.org/iso/catalogue_detail.htm?csnumber=50867
Sang J, Lei Z, Li SZ (2009) Face image quality evaluation for ISO/IEC standards 19794-5 and 29794-5. In: International conference on biometrics, no. Springer, Berlin, pp 229–238
Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst Appl 47:23–34
Abaza A, Harrison MA, Bourlai T, Ross A (2014) Design and evaluation of photometric image quality measures for effective face recognition. IET Biomet 3(4):314–324
Kocjan P, Saeed K (2012) Face recognition in unconstrained environment. In: Biometrics and Kansei engineering, no. Springer, New York, pp 21–42
Wasnik P, Raja KB, Ramachandra R, Busch C (2017) Assessing face image quality for smartphone based face recognition system. In: 2017 5th international workshop on biometrics and forensics (IWBF), no. IEEE, pp 1–6
Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on faces in ‘Real-Life’ images: detection, alignment, and recognition
Lunerti C, Guest RM, Blanco-Gonzalo R, Sanchez-Reillo R, Baker J (2017) Environmental effects on face recognition in smartphones. In: 2017 international Carnahan conference on security technology (ICCST), no. IEEE, pp 1–6
Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput 57:137–154
NEUROtechnology VeriLook 10.0 Standard SDK [Online]. Available: http://www.neurotechnology.com/verilook.html
Canon, EOS 30D specifications [Online]. Available: http://web.canon.jp/imaging/eos30d/spec/index.html
Ubergizmo, Google Nexus 5 specifications [Online]. Available: https://www.ubergizmo.com/products/lang/en_us/devices/nexus-5/
Hawkins M (2017) The exposure triangle: aperture, shutter speed and ISO explained, 12 July 2017 [Online]. Available: https://www.techradar.com/uk/how-to/photography-video-capture/cameras/the-exposure-triangle-aperture-shutter-speed-and-iso-explained-1320830
ISO/IEC TR 29794-5:2010—Information technology—biometric sample quality—part 5: face image data [Online]. Available: https://www.iso.org/standard/50912.html
Matkovic K, Neumann L, Neumann A, Psik T, Purgathofer W (2005) Global contrast factor-a new approach to image contrast. Comput Aesthetic pp 159–168
Crete F, Dolmiere T, Ladret P, Nicolas M (2007) The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: Human vision and electronic imaging XII, vol 6492, no. International Society for Optics and Photonics, p 64920I
Gonzalez RC, Eddins SL, Woods RE (2004) Representation and description. In: Digital image processing using MATLAB. Prentice Hall, Upper Saddle River, pp 465–466 (Chapter 11)
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Lunerti, C., Guest, R., Blanco-Gonzalo, R., Sanchez-Reillo, R. (2019). Selfies for Mobile Biometrics: Sample Quality in Unconstrained Environments. In: Rattani, A., Derakhshani, R., Ross, A. (eds) Selfie Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-26972-2_7
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DOI: https://doi.org/10.1007/978-3-030-26972-2_7
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