Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform
(Sprache: Englisch)
Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing,...
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Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing, feature extraction and matching. Biometric techniques are widely used in the security world. The various types of biometric systems use different techniques for the preprocessing, feature extraction and classifiers.The dorsum of the hand is known as the finger back surface. It is highly used for personal authentication and has not yet attracted the attention of convenient researchers. It is mostly used due to contact free image acquisition. It is reported that the skin pattern on the finger-knuckle is extremely rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Furthermore, advantages of using Finger Knuckle Print (FKP) include rich in texture features, easily accessible, contact-less image acquisition, invariant to emotions and other behavioral aspects such as tiredness, stable features and acceptability in the society. As a result of that, there is less known use of finger knuckle pattern in commercial or civilian applications.The local features of an enhanced palmprint image are extracted using Fast Discrete Orthonormal Stockwell Transform (FDOST). The Fourier transform of an image is obtained by increasing the scale of FDOST to infinity. The Fourier transform coefficients extracted from the palmprint image and FKP image are considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The global feature is exploited to refine the arrangement of FKP images in matching. The proposed schemes make use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance of two
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FKP images. The investigational results indicate that the proposed works outperform the existing works.
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Text Sample:Chapter 1.9 Biometric Datasets
1.9.1 College of Engineering - Pune (COEP) Palmprint Datasets
The COEP palmprint database (COEP Palm Print Database (College of Engineering Pune) 2010) consists of 8 different images of single person's palm. The database consists of total 1344 images pertaining to 168 persons. The dataset is collected over a period of one year. The images were captured using digital camera. The resolution of images is 1600×1200 pixels.
1.9.2 The PolyU Palmprint Datasets
The PolyU Palmprint Database (Zhang 2010) contains 7752 gray scale images corresponding to 386 different palms in BMP (Bitmap) image format. Twenty samples are collected from each of these palms in two sessions. Each 10 samples were captured in the first session and the session, correspondingly. The average intermission among the first and the second collection is two months period of time.
1.9.3 Indian Institute of Technology (IIT Delhi) Touchless Palmprint Datasets
The IIT-Delhi palmprint image database (Kumar 2007) consists of the hand images collected from the students and staff at IIT-Delhi, India. This dataset is acquired in the IIT Delhi campus during July 2006 - Jun 2007 using a simple and touchless imaging setup. The images are collected in the indoor atmosphere and employ circular fluorescent illumination around the camera lens. The presently accessible dataset is from 235 users. All the subjects in the database are in the age group 12-57 years. In each subject, seven images are collected from each of the left and right hand. All the images were collected in fluctuating hand posture differences. All the subjects are offered with live feedback to present the person's hand in the imaging region. The touchless imaging consequences in higher image scale variations. The resolution of these images is 800 × 600 pixels and all these images are available in bitmap format. Finally 150 × 150 pixels are automatically cropped and normalized palmprint images are also
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available.
1.9.4 The PolyU Finger Knuckle-Print Datasets
PolyU FKP database (Zhang 2009) consists of 7920 images collected from 660 different fingers. The samples are collected in two separate sessions. In each session; six images are collected for the left index and left middle finger, the right index and right middle finger. From each person, 48 images are collected from 4 fingers. The size of the acquired FKP images is 768×576 under resolution above 400 dpi. Based on the experiments, high resolution images are not necessary for feature extraction and pattern matching. Therefore, Gaussian smoothing operation is applied to the original image. The smoothen image is down sampled to about 150 dpi. Hence the size of ROI images is 110×220 pixels.
1.10 Performance Metrics
Performance testing comprises a critical aspect of biometric modality assessments. Investigators are able to draw from a wide range of performance evaluation metrics that assess functional system accuracy and usability. The choice of metrics employed in performance testing is considered by the type of biometric modality or system undergoing evaluation precisely, whether the scheme is traditional in nature (i.e. a well-established, single transaction identification modality such as Fingerprint, Face, or Iris recognition) or novel in nature (e.g. an emerging modality such as Pulse, or an innovative application such as cognitive biometrics).
Traditional performance metrics describe system accuracy, precision and usability. The ability of an authentication system to measure a biometric with a high degree of closeness to the biometrics' true value is known as accuracy. The repeatability of accurate system measurements over time is known as precision. The ease with which a system used is termed as usability. The majority of traditional biometric performance metrics derives from signal detection theory. It seeks to quantify the ability to discern between information-bearing energy patterns (signals) and
1.9.4 The PolyU Finger Knuckle-Print Datasets
PolyU FKP database (Zhang 2009) consists of 7920 images collected from 660 different fingers. The samples are collected in two separate sessions. In each session; six images are collected for the left index and left middle finger, the right index and right middle finger. From each person, 48 images are collected from 4 fingers. The size of the acquired FKP images is 768×576 under resolution above 400 dpi. Based on the experiments, high resolution images are not necessary for feature extraction and pattern matching. Therefore, Gaussian smoothing operation is applied to the original image. The smoothen image is down sampled to about 150 dpi. Hence the size of ROI images is 110×220 pixels.
1.10 Performance Metrics
Performance testing comprises a critical aspect of biometric modality assessments. Investigators are able to draw from a wide range of performance evaluation metrics that assess functional system accuracy and usability. The choice of metrics employed in performance testing is considered by the type of biometric modality or system undergoing evaluation precisely, whether the scheme is traditional in nature (i.e. a well-established, single transaction identification modality such as Fingerprint, Face, or Iris recognition) or novel in nature (e.g. an emerging modality such as Pulse, or an innovative application such as cognitive biometrics).
Traditional performance metrics describe system accuracy, precision and usability. The ability of an authentication system to measure a biometric with a high degree of closeness to the biometrics' true value is known as accuracy. The repeatability of accurate system measurements over time is known as precision. The ease with which a system used is termed as usability. The majority of traditional biometric performance metrics derives from signal detection theory. It seeks to quantify the ability to discern between information-bearing energy patterns (signals) and
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Bibliographische Angaben
- Autoren: N.B. Mahesh Kumar , K. Premalatha
- 2017, 48 Seiten, 28 Abbildungen, Masse: 15,5 x 22 cm, Kartoniert (TB), Englisch
- Verlag: Anchor Academic Publishing
- ISBN-10: 3960672039
- ISBN-13: 9783960672036
Sprache:
Englisch
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