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A review of biometric technology along with trends and prospects

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1 A review of biometric technology along with trends and prospects
J.A.Unar, Woo Chaw Seng, Almas Abbasi Adviser:Frank. Yeong-Sung Lin Present by Limin Zheng

2 Agenda Introduction Biometric systems Biometric modalities
Multi-modal biometrics Biometrics prospects Conclusion

3 Introdution

4 Introdution The ever increasing criminal and terrorist acts in public places, government/private properties resulted in chaotic scenarios thereby making the existing surveillance systems questionable – “How effective is the surveillance system to prevent unlawful activities?” “Does the system guarantee the required level of security?” “Does the system distinguish an unauthorized user from an impostor reliably?” “Is the system user friendly?”.

5 Introdution In early stages, the identity manage- ment systems relied on cryptographic methods requiring the users to remember a secret text (password) or keep something with them (token, card) or a combination of both to prove their identity. Access control, computer logins, checking, making bank transactions, border control, welfare disbursements etc. However, the users challenged the efficacy of such systems.

6 Introdution As an answer, the research community proposed the idea of human identification based on physiological or behavioral attributes of individuals very often termed as “Biometrics”. Although biometrics is not the perfect solution but it offers several advantages over knowledge and possession based approaches in the way that there is no need to remember anything, biometric attributes can not be lost, transferred or stolen, offer better security due to fact that these attributes are very difficult to forge and require the presence of genuine user while granting access to particular resources.

7 Introdution Any physiological or behavioral attribute can qualify for being a biometric trait unless it satisfies the criteria such as Universality Distinctiveness Invariance Collectability Performance Acceptability Circumvention


9 Introdution The paper provides details about each biometric modality along with prominent recognition techniques and public data sets available to the researchers. The article contains a rough quantitative analysis of individual techniques in terms of accuracy. The paper discusses multi-modal biometrics in detail which has not been well covered in other review articles on the subject. The paper contains current market statistics along with future prospects for biometric based identification and authentication.

10 Biometric systems

11 Biometric systems A biometric system is a pattern recognition system which matches the salient or discriminatory features of acquired image (probe image) with the features of pre-stored images (gallery image). For doing so, every biometric system comprises of Image acquisition module Feature extraction module Matcher module Database module

12 Biometric systems 2.1. Modes of operation
2.1.1.Verification (positive recognition) The user after submitting the biometric signature to the system claims a parti- cular identity through a PIN, login name, etc. Recognition system validates or voids user's claim by making a 1:1 (one-to-one) comparison between the submitted biometric signa- ture and enrolled biometric signature associated with that parti- cular identity.

13 Biometric systems 2.1. Modes of operation
2.1.2.Identification (negative recognition) Recognition system tries to recognize the user by comparing the submitted biometric signature to all the enrolled signatures in the database by making 1:N (one-to-many) comparisons without specific identity claim from the user.

14 Biometric systems 2.1. Modes of operation
Screening This is an extension to identification where the biometric system assures that a particular individual does not belong to a watch list of identities by performing 1:N (one-to-many) comparisons throughout the database.

15 Biometric systems 2.1. Modes of operation
Application Verification Computer logins, ATMs, e-commerce, access control and user authentication on mobile devices [2]. Identification Issuance of ID cards, passports, riving licenses, border crossing and welfare disbursements [1,2]. Screening Airport security, surveillance activities, public place and public events security etc. [4]

16 Biometric systems 2.2. Performance measurements
The performance or accuracy of a biometric system is data dependent usually influenced by environmental and performance factors. Environmental Factors: Temperature, humidity and illumination conditions Performance Factors: Capturing good quality images, composition of target user population, time interval between the enrollment and verification phases and robustness of recognition algorithms [2].

17 Biometric systems 2.2. Performance measurements
Sample acquisition errors Environmental conditions surrounding the system give rise to acquisition errors. Failure to enroll error Failure to capture error

18 Biometric systems 2.2. Performance measurements
Performance errors These errors are used to measure the accuracy of biometric systems in real environment. A brief description is given below: False match rate (FMR) or false accept rate (FAR) Falsenon – match rate(FNMR)orfalserejectrate(FRR) Equal error rate (EER)

19 Biometric modalities

20 Thissectionprovidesanin-depthoverviewofdifferentbiometric modalitiesaspertheclassificationprovidedin Fig. 1 above.

21 Biometric modalities 3.1. Hand region modalities
Fingerprints(BEST) Palmprint Hand geometry Finger knuckle print Finger nail bed Hand vein pattern

22 Biometric modalities 3.1. Hand region modalities

23 Biometric modalities 3.1. Hand region modalities
Image acquisition methods: Optical Thermal Silicon Ultrasonic imaging Exceptions: Hand geometry → use a camera to acquire 3D image Hand vein → beneath the skin thereby exposing hand to near infrared light

24 Biometric modalities 3.1. Hand region modalities
Advantage: low cost imaging sensors small template size Disadvantage: artificial gummy fingers → Recent trend : vein technology

25 Biometric modalities 3.2. Facial region modalities
Face Ear shape Teeth Tongue print

26 Biometric modalities 3.2. Facial region modalities
3D:However, due to the challenges stated above as well as medium level of distinctiveness, facial recognition systems are very much prone to recognition errors. The potential advantages associated with tongue print include protection from external environment and its squirm which proves the liveliness of the subject. The conformity of tongue print as biometric attribute requires large scale studies. 牙齒部分主要用在法醫界~ 作者認為超出範圍,故不進一步探討

27 Biometric modalities 3.2. Facial region modalities
Image acquisition methods: 2D 3D Facial thermograph localization and segmentation

28 Biometric modalities 3.2. Facial region modalities
Advantage: The most nature non-intrusive contactless Disadvantage: cosmetics plastic surgery person's face may change or be changed over time expensive imaging hardware illumination conditions, pose variations , ageing conditions poor recognition accuracy higher public acceptance

29 Biometric modalities 3.3. Ocular region modalities
Iris Retina Sclera possesses most accurate,highly reliable,well protected,stable and almost impossible to forge biometric signatures,

30 Biometric modalities 3.3. Ocular region modalities

31 Biometric modalities 3.3. Ocular region modalities
Image acquisition methods: infra-red light(retina, sclera ) illuminating human eye with near infra-red light or in visible wavelength light(iris) position and bifurcations of blood vessels measuring the area of reference (fovea, optic disk) (Sclera)Consequently, the conformity requires large scale studies. IRIS相較非侵入 獲勝!!! retinal scan systems 只用在軍事目的

32 Biometric modalities 3.3. Ocular region modalities
Advantage: Highly accurate (higher uniqueness) Highly reliable Well protected Stable Almost impossible to forge Disadvantage: synthetic iris images (need iris codes ) intrusive (retina)

33 Biometric modalities 3.4. Medico-chemical biometrics
Body odor → not realized yet Deoxyribonucleic Acid(DNA) Heart sound Electrocardiogram (ECG) (body odor) albeit some commercial products are already available in the market known as e-noses which can recognize the particular odor such as gases and vapors

34 Biometric modalities 3.4. Medico-chemical biometrics
Image acquisition methods: Specialized medical/chemical sensors Samples from blood, hair, ear wax, dental floss and finger nail clippings(DNA) Different organic compounds(body odor)

35 Biometric modalities 3.4. Medico-chemical biometrics
Advantage: The Most accurate (DNA) Disadvantage: Intrusive Privacy issues Physical contact with sensors Requirement of skilled operators Full cooperation from the subjects Dependence over human medical and emotional states these of the restrictions towards large scale deployment of such systems.

36 Biometric modalities 3.5. Behavioral biometrics
Keystroke Dynamics Voice Signature Gait based on the analysis of the way humans do the things.

37 Biometric modalities 3.5. Behavioral biometrics
Voice fixed vocabulary constraints (Gait) Recently, user authenti- cation based on gait analysis has been applied on mobile devices which in turn produced another research arena in gait biometrics. (Gait)用在手機上,手機必須有加速度計 這些方法已經開發許久,作者認為其也有不錯的準確度 但有人認為 這些作法無法提供充足的獨特性,因為人的習慣及健康狀態與飲食習慣還有老化程度都會影響行為。 還有像是背景噪音之類的。肌肉受損也會簽的跟原先不同。此外,步態因為是動態的,也有著很高的誤判綠。 無庸置疑的是,人類的行為模式相較之下容易模仿,有些更是十分專業。 因此也只能用來做認證用而已

38 Biometric modalities 3.5. Behavioral biometrics
Acquisition methods: Video Sound Recording Electronic whiteboard

39 Biometric modalities 3.5. Behavioral biometrics
Advantage: Easily for acquisition Disadvantage: Dynamics Mimicking


41 Biometric modalities 3.6. Soft biometrics
Gender Ethnicity Height Weight Skin color Eye color Hair color SMT(scars, marks and tattoos) 雖然這些Soft的生物辨識特徵,無法提供有效率的鑑別去完成辨識任務。 但它們可以用於分類,能縮減辨識所花的時間。 紋身辨識在法醫領域也一度很夯,許多非法組織會紋身。也很多人用紋身來表明自己的信仰。 所以紋身的資料庫其實也有機構在蒐集。 一個非常典型的例子就是,辨別911恐怖攻擊與南亞大海嘯的受害者。 因為刺青是很深的烙印,有時就算皮膚被烤焦也無法使其完全消失。 作者也有做過紋身辨識系統,但依照先前說的生物辨識要素李,紋身並不符合普遍性。 此外,很多研究申稱結合Soft biometric能提高Hard biometric的準確度

42 Biometric modalities 3.7. Application perspectives
The recentyearswitnessedmassdeploymentofbiometric technologiesforreliablehumanidentification inpublicaswell as privatesector.Nevertheless,theapplicationenvironmentsfor each technologyaresummarizedinthefollowingtable Table6. A closerlookrevealsthatmostofthetechniquesinorderto establish theidentityrequireclosecontactofthesubjectwiththe imaging sensor.However,facerecognitionandrecognitionbased on gaithasthepotentialtoidentifyhumansatadistance.Asa result,facerecognitionandgaitrecognitionsuitsmorethevideo surveillanceactivitiessuchassuspectidentification atairportsas wellascrowdmonitoringandhumanactivityrecognitionatpublic places whichisnotpossiblewithotherbiometrictechniquessuch as fingerprint,irisrecognitionwhichrequirededicatedarrange- ments forimagingaswellassufficient usercooperation.

43 Multi-modal biometrics

44 Multi-modal biometrics
Instead of the tremendous advances in biometric technology, the recognition systems based on the measurement of single modality can not guarantee 100% accuracy. This is due to influence of several factors. Noisy data Intra-class variations Distinctiveness Non-universality Spoof attacks mono-modal VS Multi-modal

45 Multi-modal biometrics 4.1. Sources of information
According to the nature of information sources, a multi-modal biometric system can be classified into following categories: [23,275,276] Multi-sensor systems Multi-algorithm systems Multi-instance systems Multi-sample systems Multi-modal systems mono-modal VS Multi-modal

46 Multi-modal biometrics 4.2. Modes of operations
A multi-modal biometric system functions in two modes: Serial/cascaded mode Parallel mode mono-modal VS Multi-modal

47 Multi-modal biometrics 4.3. Fusion levels
The multiple biometric evidences acquired from different sources can be combined together at different levels: Sensor level Feature level Matching score level Decision level mono-modal VS Multi-modal

48 Multi-modal biometrics 4.3. Fusion levels
mono-modal VS Multi-modal

49 Multi-modal biometrics 4.3. Fusion levels
mono-modal VS Multi-modal

50 Multi-modal biometrics 4.3. Fusion levels
mono-modal VS Multi-modal

51 Biometrics prospects

52 Biometrics prospects The increasing interest from the government and private stakeholders to adopt biometric based identity management systems prove the efficacy of such systems over the knowledge and possession based systems.

53 Biometrics prospects

54 Biometrics prospects

55 Biometrics prospects

56 Conclusion

57 Conclusion Reliable Identification is a crucial requirement in a wide variety of systems & different scenarios. Traditional cryptographic methods do not offer practical identification capabilities. As an alternative, reliable identification, very often referred as “biometrics” provide better solutions to high security demands of the present day. A wide variety of biometric characteristics have been discovered and tested in recent decades.

58 Conclusion In this paper, we provided an overview of the existing biometric technologies along with their composition, classification and performance evaluation indicators. This paper also provides an in- depth overview followed by detailed discussion over different biometric attributes along with their potential advantages and challenges. In terms of multiple attributes, careful selection is the key point to success since selecting the modalities belonging to one region may not be a good choice

59 Conclusion Furthermore, we also stress the importance of reliable template protection strategies since the development of artificial gummy fingers and synthetic irises may lead to questions regarding the reliability of the most secure biometric signatures so far.

60 Conclusion Despite the challenges, the market trend presented in the paper portrays a keen interest in biometric solutions to a number of identity management tasks. The large deployment of such systems in private as well as the stringent security environments is evident from the exemplary evidences produced here. Conclusively, the increasing trend of interest from government and private stake- holders promises a bright future to the science of biometrics.

61 Thank you for Listening

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