nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

On Designing a Forensic Toolkit for Rapid Detection of Factors that Impact Face Recognition Performance When Processing Large Scale Face Datasets

verfasst von : J. Rose, T. Bourlai

Erschienen in: Securing Social Identity in Mobile Platforms

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Due to the overlap between the fields of forensic investigation and biometric recognition, including face recognition, there have been several interesting applications that bridge the gap between the two sciences and better connect the associated communities. These applications have been developed with the intent to assist law enforcement officers with computer assisted and biometrics related capabilities. Thus, utilizing biometric algorithms within the forensics field can support law enforcement investigations in a wide array of applications, including fingerprint comparisons, sketch-to-photo face comparisons, and even find persons of interest via soft biometrics such as scars, marks, and tattoos. In this book chapter, we focus on facial recognition, which can help provide clues when other forensic evidence is not present or available and, most importantly, help investigators eliminate the time consuming processes of interviewing potential witnesses or manually searching through thousands of mugshots to determine a suspect’s identity. To aid in this mission, we propose a software toolkit to automatically and hierarchically categorize face images with a set of binary classifiers using three different attributes, which depending on their true label/condition can affect facial recognition performance. These attributes are: based on facial photo, (1) determining whether a subject’s eyes are open or closed, (2) whether the subject is wearing glasses or not, and (3) whether the facial pose of the subject is either frontal or non-frontal. Our toolkit offers batch processing and therefore can aid forensic operators with a capability to rapidly categorize large scale face datasets in terms of the aforementioned attributes, and thus, determine, which individuals have a higher chance to be identified based on their face information. The proposed forensic toolkit will allow the operators to analyze, enhance, group, or exclude face data before being used for face matching.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Personalized Data Minimization Assurance Using Bluetooth Low Energy

Nächstes Kapitel Classification of Soft Biometric Traits When Matching Near-Infrared Long-Range Face Images Against Their Visible Counterparts

Jain AK, Ross A (2015) Bridging the gap: from biometrics to forensics. Philos Trans R Soc B 370(1674):20140254CrossRef

Tistarelli M, Grosso E, Meuwly D (2014) Biometrics in forensic science: challenges, lessons and new technologies. In: International workshop on biometric authentication. Springer, pp 153–164

Meuwly D, Veldhuis R (2012) Forensic biometrics: from two communities to one discipline. In: 2012 BIOSIG-proceedings of the international conference of the biometrics special interest group (BIOSIG). IEEE, pp 1–12

Dessimoz D, Champod C (2008) Linkages between biometrics and forensic science. In: Handbook of biometrics. Springer, Boston, pp 425–459CrossRef

Champod C, Tistarelli M (2017) Biometric technologies for forensic science and policing: state of the art. In: Handbook of biometrics for forensic science. Springer, pp 1–15

Jain AK, Klare B, Park U (2011) Face recognition: some challenges in forensics. In: 2011 IEEE international conference on automatic face & gesture recognition and workshops (FG 2011). IEEE, pp 726–733

Jain AK, Klare B, Park U (2012) Face matching and retrieval in forensics applications. IEEE Multimed 19(1):20CrossRef

Lee J, Jain A, Tong W et al (2012) Image retrieval in forensics: tattoo image database application. IEEE MultiMed 19(1):40–49CrossRef

Wing BJ (2013) The ANSI/NIST-ITL standard update for 2011 (data format for the interchange of fingerprint, facial and other biometric information). Int J Biom 5(1):20–29

10.

Bourlai T (2016) Face recognition across the imaging spectrum. Springer, SwitzerlandCrossRef

11.

Bourlai T, Narang N, Cukic B, Hornak L (2012) On designing a swir multi-wavelength facial-based acquisition system. In: Infrared technology and applications XXXVIII, vol 8353. International Society for Optics and Photonics, p 83530R

12.

White D, Dunn JD, Schmid AC, Kemp RI (2015) Error rates in users of automatic face recognition software. PLoS One 10(10):e0139827CrossRef

13.

Jain AK, Li SZ (2011) Handbook of face recognition. Springer, New YorkMATH

14.

Ding X, Wang L (2011) Facial landmark localization. In: Handbook of face recognition. Springer, New York, pp 305–322CrossRef

15.

Whitelam C, Bourlai T (2015) Accurate eye localization in the short waved infrared spectrum through summation range filters. Comput Vis Image Underst 139:59–72CrossRef

16.

El-Sayed MA, Khafagy MA (2014) An identification system using eye detection based on wavelets and neural networks. 1401.5108

17.

Bourlai T, Jafri Z (2011) Eye detection in the middle-wave infrared spectrum: towards recognition in the dark. In: 2011 IEEE international workshop on information forensics and security. IEEE, pp 1–6

18.

Dutta A, Günther M, El Shafey L, Marcel S, Veldhuis R, Spreeuwers L (2015) Impact of eye detection error on face recognition performance. IET Biom 4(3):137–150CrossRef

19.

Du C, Su G (2005) Eyeglasses removal from facial images. Pattern Recogn Lett 26(14):2215–2220CrossRef

20.

Taylor M (2017) The art of facial recognition. https://www.forensicmag.com/article/2017/03/art-facial-recognition. Accessed 18 Feb 2018

21.

Mandal B, Li L, Wang GS, Lin J (2017) Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans Intell Transp Syst 18(3):545–557CrossRef

22.

González-Ortega D, Díaz-Pernas FJ, Antón-Rodríguez M, Martínez-Zarzuela M, Díez-Higuera JF (2013) Real-time vision-based eye state detection for driver alertness monitoring. Pattern Anal Appl 16(3):285–306MathSciNetCrossRef

23.

Zhao L, Wang Z, Zhang G, Qi Y, Wang X (2017) Eye state recognition based on deep integrated neural network and transfer learning. Multimed Tools Appl 77(15):1–24

24.

Kim KW, Hong HG, Nam GP, Park KR (2017) A study of deep CNN-based classification of open and closed eyes using a visible light camera sensor. Sensors 17(7):1534CrossRef

25.

Ji Y, Wang S, Lu Y, Wei J, Zhao Y (2018) Eye and mouth state detection algorithm based on contour feature extraction. J Electron Imaging 27(5):051205CrossRef

26.

Hassan H, Yaacob S, Radman A, Suandi SA (2016) Eye state detection for driver inattention based on Lucas Kanade optical flow algorithm. In: 2016 6th international conference on intelligent and advanced systems (ICIAS). IEEE, pp 1–6

27.

Radlak K, Smolka B (2012) A novel approach to the eye movement analysis using a high speed camera. In: 2012 2nd international conference on advances in computational tools for engineering applications (ACTEA). IEEE, pp 145–150

28.

Fogelton A, Benesova W (2016) Eye blink detection based on motion vectors analysis. Comput Vis Image Underst 148:23–33CrossRef

29.

Pauly L, Sankar D (2015) Detection of drowsiness based on hog features and SVM classifiers. In: 2015 IEEE international conference on research in computational intelligence and communication networks (ICRCICN). IEEE, pp 181–186

30.

Dong Y, Zhang Y, Yue J, Hu Z (2016) Comparison of random forest, random ferns and support vector machine for eye state classification. Multimed Tools Appl 75(19):11763–11783CrossRef

31.

Eddine BD, Dos Santos FN, Boulebtateche B, Bensaoula S (2018) Eyelsd a robust approach for eye localization and state detection. J Signal Process Syst 90(1):99–125CrossRef

32.

Sharif M, Bhagavatula S, Bauer L, Reiter MK (2016) Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. ACM, pp 1528–1540

33.

Ying H, Da Su G, Chen JS (2014) Automatic eyeglasses removal of frontal facial images for face recognition. Appl Mech Mater 571:697CrossRef

34.

Alorf A, Abbott AL (2017) In defense of low-level structural features and SVMs for facial attribute classification: application to detection of eye state, mouth state, and eyeglasses in the wild. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 599–607

35.

Mohammad AS, Rattani A, Derahkshani R (2017) Eyeglasses detection based on learning and non-learning based classification schemes. In: 2017 IEEE international symposium on technologies for homeland security (HST). IEEE, pp 1–5

36.

Zhao M, Zhang X, Shi Z, Chen T, Zhang F (2018) Eyeglasses detection, location and frame discriminant based on edge information projection. Multimed Tools Appl 77(12):14931–14949CrossRef

37.

Lazarus MZ, Gupta S (2016) A low rank model based improved eye detection under spectacles. In: Ubiquitous computing, electronics & mobile communication conference (UEMCON), IEEE annual. IEEE, pp 1–6

38.

Basbrain AM, Al-Taie I, Azeez N, Gan JQ, Clark A (2017) Shallow convolutional neural network for eyeglasses detection in facial images. In: Computer science and electronic engineering (CEEC), 2017. IEEE, pp 157–161

39.

Wang Y, Ou X, Tu L, Liu L (2018) Effective facial obstructions removal with enhanced cycle-consistent generative adversarial networks. In: International conference on artificial intelligence: methodology, systems, and applications. Springer, pp 210–220

40.

Zhang X, Pham DS, Venkatesh S, Liu W, Phung D (2015) Mixed-norm sparse representation for multi view face recognition. Pattern Recognition 48(9):2935–2946.CrossRef

41.

Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process 24(3):980–993MathSciNetCrossRef

42.

Zhang X, Gao Y (2009) Face recognition across pose: A review. Pattern Recogn 42(11):2876–2896CrossRef

43.

Zhang X, Pham D-S, Venkatesh S, Liu W, Phung D (2015) Mixed-norm sparse representation for multi view face recognition. Pattern Recogn 48(9):2935–2946CrossRef

44.

Ho HT, Chellappa R (2013) Pose-invariant face recognition using Markov random fields. IEEE Trans Image Process 22(4):1573–1584MathSciNetCrossRef

45.

Xu W, Shen Y, Bergmann N, Hu W (2018) Sensor-assisted multi-view face recognition system on smart glass. IEEE Trans Mobile Comput 17(1):197–210CrossRef

46.

Shao M, Zhang Y, Fu Y (2018) Collaborative random faces-guided encoders for pose-invariant face representation learning. IEEE Trans Neural Netw Learn Syst 29(4):1019–1032CrossRef

47.

Oh B-S, Toh K-A, Teoh ABJ, Lin Z (2018) An analytic gabor feedforward network for single-sample and pose-invariant face recognition. IEEE Trans Image Process 27(6):2791–2805MathSciNetCrossRef

48.

Deng W, Hu J, Wu Z, Guo J (2017) Lighting-aware face frontalization for unconstrained face recognition. Pattern Recogn 68:260–271CrossRef

49.

Masi I, Chang F-J, Choi J, Harel S, Kim J, Kim K, Leksut J, Rawls S, Wu Y, Hassner T et al (2018) Learning pose-aware models for pose-invariant face recognition in the wild. IEEE Trans Pattern Anal Mach Intell 41(2):379–393CrossRef

50.

Abaza A, Harrison MA, Bourlai T, Ross A (2014) Design and evaluation of photometric image quality measures for effective face recognition. IET Biom 3(4):314–324CrossRef

51.

Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503CrossRef

52.

Viola P, Jones M (2001, December) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, vol 1. IEEE, pp I–I

53.

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

54.

Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893

55.

Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59CrossRef

56.

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH

57.

Fix E, Hodges JL Jr (1951) Discriminatory analysis-nonparametric discrimination: consistency properties. Technical report, California Univ BerkeleyMATH

58.

Breiman L (2017) Classification and regression trees. Routledge, BelmontCrossRef

59.

Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems. Springer, pp 1–15

60.

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

61.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

Titel: On Designing a Forensic Toolkit for Rapid Detection of Factors that Impact Face Recognition Performance When Processing Large Scale Face Datasets
verfasst von: J. Rose
T. Bourlai
Verlag: Springer International Publishing
Buch: Securing Social Identity in Mobile Platforms
Print ISBN: 978-3-030-39488-2

Electronic ISBN: 978-3-030-39489-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39489-9_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"