Biometrics and its applications for system Analysis
- 1. Dr. N.G.P. Institute of Technology Department of Biomedical Engineering 22OBM103 – Biometrics and its Application Dr. R. Mothi Assistant Professor Department of Biomedical Engineering
- 4. Biometrics? • Biometrics measures the unique physical and behavioral characteristics of a living creature. • Recognizing a person based on a physiological or behavioral characteristics. Life Measurement
- 5. Biometrics? The physical attributes such as finger prints, color of iris, color of hair, hand geometry, and behavioral characteristics such as tone and accent of speech, signature, or the way of typing keys of computer keyboard etc., make a person stand separate from the rest. This uniqueness of a person is then used by the biometric systems to • Identify and verify a person. • Authenticate a person to give appropriate rights of system operations. • Keep the system safe from unethical handling.
- 6. Evolution of Biometric System Time Period Biometric Technology Key Development Inventor(s) Origin Country Late 19th Century Fingerprint Recognition The concept of using fingerprints as a unique identifier for individuals. Sir Francis Galton (First to study fingerprint patterns) United Kingdom 1892 Fingerprint Identification System The use of fingerprints for criminal identification in Argentina. Juan Vucetich (Developed fingerprint-based system for police work) Argentina 1890 Anthropometry (Bertillonage) A system of identifying criminals using physical measurements. Alphonse Bertillon France 1930s - 1940s Iris Recognition Early experiments on using the iris as a unique biometric identifier. Sir William Herschel (Initiated iris studies for identification) United Kingdom 1960s Facial Recognition The development of early facial recognition algorithms for identity verification. Woodrow W. Bledsoe, Helen Chan Wolf United States 1960s Voice Recognition Early work in voice recognition and speaker verification systems. Davis S. R. (Pioneering research in voiceprints) United States 1970s Retina Scanning Exploration of retina scanning as a biometric identifier. Not attributed to a single inventor United States 1980s Automated Fingerprint Identification Systems (AFIS) Introduction of digital fingerprint matching systems for law enforcement. Various researchers (including W. B. Scharf and others for AFIS development) United States 1990s Iris and Retinal Scanning Advancements in iris and retinal scanning systems for secure identification. John Daugman (Iris recognition algorithm) United Kingdom
- 7. 1990s Facial Recognition (Algorithms) The development of facial recognition algorithms for practical use. F. M. Bonneau, S. B. Kang United States / United Kingdom 2000s Fingerprint Scanning in Mobile Devices Integration of fingerprint scanning for mobile devices like smartphones. Apple Inc. (Pioneered fingerprint sensor in mobile devices) United States 2000s Voice Recognition in Consumer Devices Widespread use of voice recognition for consumer applications. Various developers (e.g., Amazon and Google for voice assistants) United States 2010s Facial Recognition in Smartphones Launch of Face ID and adoption of facial recognition technology in smartphones. Apple Inc. (Developed Face ID for iPhone) United States 2010s Multi-modal Biometric Systems The combination of multiple biometric technologies, such as fingerprints and facial recognition, for enhanced security. Various research teams Global (Various countries) 2020s Behavioral Biometrics Development of behavioral biometrics, including the analysis of typing patterns and user behavior. Various researchers United States / United Kingdom 2020s Liveness Detection Use of advanced techniques in liveness detection to prevent spoofing in biometric systems. Various developers (e.g., IDEX Biometrics for anti-spoofing technology) Global (Various countries) Evolution of Biometric System Time Period Biometric Technology Key Development Inventor(s) Origin Country
- 8. Why Biometrics is Required? Biometrics is used for authenticating and authorizing a person. Authentication (Identification) • This process tries to find out answer of question, “Are you the same who you are claiming to be?”, or, “Do I know you?” This is one-to-many matching and comparison of a person’s biometrics with the whole database. Verification • This is the one-to-one process of matching where live sample entered by the candidate is compared with a previously stored template in the database. Authorization It is the process of assigning access rights to the authenticated or verified users.
- 9. Shortcoming of Conventional Aids? • They all mean recognizing some code associated with the person rather than recognizing the person who actually produced it. • They can be forgotten, lost, or stolen. • They can be bypassed or easily compromised. • They are not precise. The security of the system is threatened. When the systems need high level of reliable protection, biometrics comes to help by binding the identity more oriented to individual.
- 10. Biometric System Sensors Converts human biological data into digital form. • A Metal Oxide Semiconductor (CMOS) imager or a Charge Coupled Device (CCD) in the case of face recognition, handprint recognition, or iris/retinal recognition systems. • An optical sensor in case of fingerprint systems. • A microphone in case of voice recognition systems. Processing Unit Microprocessor, Digital Signal Processor (DSP), or computer that processes the data captured from the sensors • Sample image enhancement • Sample image normalization • Feature extraction • Comparison of the biometric sample with all stored samples in database. Database Store The database stores the enrolled sample, which is recalled to perform a match at the time of authentication. For identification, there can be any memory from Random Access Memory (RAM), flash EPROM, or a data server. Output Interface The output interface communicates the decision of the biometric system to enable the access to the user.
- 11. Working of Biometric System 1. Acquire live sample from candidate. (using sensors) 2. Extract prominent features from sample. (using processing unit) 3. Compare live sample with samples stored in database. (using algorithms) 4. Present the decision. (Accept or reject the candidate.) Two Modes of Operation Identification – One to Many Comparison of the captured biometric against a biometric database in attempt to identify an unknown individual. Verification – One to One Comparison of a captured biometric with a stored template to verify that the individual is who he claims to be.
- 12. Types of Biometric Modalities Biometric Modality Person’s Biological Traits • This modality pertains to the shape and size of the body. Physiological • This modality is related to change in human behavior over time. Behavioural • This modality includes both traits, where the traits are depending upon physical as well as behavioral changes. Combination of physiological and behavioral modality
- 13. Laws of Biometrics Three Laws
- 17. There are three basic patterns of ridges namely, arch, loop, and whorl. The uniqueness of fingerprint is determined by these features as well as minutiae features such as bifurcation and spots (ridge endings) Minutiae Based Techniques − In these minutiae points are found and then mapped to their relative position on finger. There are some difficulties such as if image is of low quality, then it is difficult to find minutiae points correctly. Another difficulty is, it considers local position of ridges and furrows; not global. Correlation Based Method − It uses richer gray scale information. It overcomes problems of minutiae-based method, by being able to work with bad quality data. Pattern Based (Image Based) Matching − Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a stored template and a candidate fingerprint. Fingerprint Biometrics – Unit 2 • Finger print using vein pattern of palm • Fingerprint Biometrics • Fingerprint recognition system • minutiae extraction • advantage and disadvantages of fingerprint biometrics.
- 18. Facial Recognition • Facial recognition is based on determining shape and size of jaw, chin, shape and location of the eyes, eyebrows, nose, lips, and cheekbones. • 2D facial scanners start reading face geometry and recording it on the grid. • Facial geometry is transferred to the database in terms of points. • The comparison algorithms perform face matching and come up with the results. Steps Involved in Facial Recognition Facial recognition is performed in the following ways: • Facial Metrics − In this type, the distances between pupils or from nose to lip or chin are measured. • Eigen faces − It is the process of analyzing the overall face image as a weighted combination of a number of faces. • Skin Texture Analysis − The unique lines, patterns, and spots apparent in a person’s skin are located.
- 19. Facial Recognition – Unit 3 • Background of face recognition • Design of face recognition system • Neural network for face recognition • Face detection in video sequences • Face recognition methods.
- 20. Iris Recognition is a biometric technology that uses the unique patterns in the colored part of the eye (the iris) to identify or verify individuals. The iris, located around the pupil, has intricate and distinctive features that remain stable throughout a person's lifetime, making it an excellent trait for biometric identification. Key Features of Iris Recognition: •Uniqueness: The iris patterns are unique to each individual, even between identical twins, and are highly stable over time. •Stability: Unlike fingerprints or facial features, the iris does not change significantly over a person's lifetime. •Accuracy: Iris recognition systems are considered one of the most accurate biometric technologies, with a low False Acceptance Rate (FAR) and False Rejection Rate (FRR). IRIS Recognition
- 21. IRIS Recognition Hand Geometry Recognition IRIS RECOGNITION & HAND GEOMETRY – Unit 4 Design of iris recognition system – iris segmentation method – application of iris biometrics – basics of hand geometry – image capturing, hand segmentation, feature extraction https://youtu.be/F820W0_PBwo?si=pW284J9lZ-8a5vrC
- 22. Multimodal Biometrics Multimodal Biometrics refers to the use of multiple biometric traits or modalities (such as fingerprints, facial recognition, iris patterns, voice, etc.) for identification or authentication. Instead of relying on a single biometric trait, a multimodal system combines two or more biometrics to enhance accuracy, security, and reliability. Fusion in Multimodal Biometrics refers to the process of combining data or results from multiple biometric modalities (such as fingerprints, facial recognition, iris, voice, etc.). Unit -5
- 23. 22OBM103 – BIOMETRICS AND ITS APPLICATIONS UNIT I BIOMETRICS SYSTEM 9 History of Biometrics – Types of Biometric Traits – General Architecture of Biometric Systems – Basic working of Biometric matching – Biometric system error and performance measure. UNIT II FINGERPRINT BIOMETRICS 9 Finger print using vein pattern of palm – Fingerprint Biometrics – Fingerprint recognition system – minutiae extraction – advantage and disadvantages of fingerprint biometrics. UNIT III FACE RECOGNITION 9 Background of face recognition – design of face recognition system - neural network for face recognition – face detection in video sequences – face recognition methods. UNIT IV IRIS RECOGNITION & HAND GEOMETRY 9 Design of iris recognition system – iris segmentation method – application of iris biometrics – basics of hand geometry – image capturing, hand segmentation, feature extraction. UNIT V MULTIMODAL BIOMETRICS & BIOMETRIC APPLICATIONS 9 Basic architecture of multimodal biometrics – multimodal biometrics using face and ear – multimodal biometrics application – case study of biometric application. Total Hours 45 Course outcomes: At the end of the course, students will be able to CO1: Demonstrate knowledge on biometric authentication system using biometric applications. CO2: Explain the fingerprint technology using fingerprint enhancement, feature extraction, classification and matching technique used for criminal application. CO3: Design face recognition system using neural network, video sequences used for various biometric applications CO4: Describe about iris recognition and hand geometry using segmentation and feature extraction technique used for commercial application. CO5: Identify issues in multimodal biometrics using face and ear used for biometric application.