Fingerprint image enhancement and recognition algorithms: a survey

Hasan, Haitham; Kareem, Sameem Abdul

doi:10.1007/s00521-012-1113-0

Cited by 45 publications

(21 citation statements)

References 18 publications

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“…The first approach employs sensors (mechanical or optical) attached 2 The cyborg glove: data glove is constructed with stretch fabric for comfort and a mesh Palm for ventilation [11] to a glove that acts as transducer of finger flexion into electrical signals to determine hand posture, as shown in Fig. 2.…”

Section: Hand Gesture Analysis Approachesmentioning

confidence: 99%

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RETRACTED ARTICLE: A Survey on Recent Vision-Based Gesture Recognition

Badi

2016

Intell Ind Syst

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Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified in two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human-computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.

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Section: Hand Gesture Analysis Approachesmentioning

confidence: 99%

“…To utilize this new phenomenon efficiently, many studies have examined computer applications and their requirement of increased interaction. Thus, human computer interaction (HCI) has been a lively field of research [2,3].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A Survey on Recent Vision-Based Gesture Recognition

Badi

2016

Intell Ind Syst

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“…The pattern of the ridges and furrows of the fingers are unique for each individual. Therefore, based on the pattern of the ridges, furrows and minutiae points a person is identified or verified (Hasan et al 2013). The advantage of fingerprint biometric is that it is socially acceptable, easy to collect the fingerprint and even the fingerprint of each finger on the same hand are different from each other.…”

Section: Fingerprintmentioning

confidence: 99%

A Survey on Biometrics and Cancelable Biometrics Systems

Choudhury

Then

Issac

et al. 2018

Int. J. Image Grap.

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Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.Key Words: Biometrics, Cancelable Biometrics, Security, Biometric Salting, Non-Invertible Transformation INTRODUCTIONThe biometric traits possessed by each individual are unique and has the potential to recognize an individual. Biometric traits can be physical and behavioral. Therefore, biometrics are used for authentication or recognition of individuals for many critical applications like border control, access control, immigration, forensic and different law enforcement. There are two phases in every conventional biometric system: enrolment phase and authentication phase. In the enrolment stage, the original biometric trait is captured and saved in the database. During the authentication stage, the system matches that stored template every time the user access the system by providing the live biometric (Kaur et al. 2014).Compared to password or token based authentication system, biometric system using fingerprint, iris, face, voice, etc. provides better security as people cannot lose or forget their biometric trait. But, the advanced technology of today's world makes it possible to create a loophole in the biometric system. People leave their fingerprints on whatever they touch; hence one can easily steal the fingerprint and can even make an artificial finger using the stolen fingerprint. The person's face can be captured by the camera even from a distance without their concern. In such situation, the security of the biometric based authentication system is at a stake.To overcome the problem of the stolen biometrics, the researchers have developed the template protection schemes. The biometric template protection schemes are mainly divided into two categories: i) Biomet...

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“…Thus, an ANN has been a lively field of research [16,17]. A key can be thought of as some piece of confidential user information in relation to an application such that the latter will perform as intended only after the former is input correctly [18].…”

Section: Key Securitymentioning

confidence: 99%

Encryption function on artificial neural network

Azawee

Husien

Yunus

2015

Neural Comput & Applic

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Six algorithms that design secrecy keys are used for digital image encryption. The privacy keys generated by stream cipher generators were tested for their randomness applying five tests of randomness. The images generated by applying each algorithm were tested for their regularity and residual intelligibility. The histograms for images ciphered by stream cipher schemes all have approximately flat histogram less information as compared to one-dimensional ciphering algorithms and threshold generator-based image ciphering scheme. Furthermore, the stream cipher schemes were effectively applied to the colored images of 256-color levels of true-color images (24 bit). In this paper, we try to decrypt automatically using artificial neural network by decryption through multilayer perceptron and radial basis function; networks were tested using the interface by calculating the error rates of decrypted images.

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Fingerprint image enhancement and recognition algorithms: a survey

Cited by 45 publications

References 18 publications

RETRACTED ARTICLE: A Survey on Recent Vision-Based Gesture Recognition

RETRACTED ARTICLE: A Survey on Recent Vision-Based Gesture Recognition

A Survey on Biometrics and Cancelable Biometrics Systems

Encryption function on artificial neural network

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