Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.

Unimodal and Multimodal Biometric Sensing Systems: A Review

While we are experiencing many advantages of digital technologies and products, the security issues are also attracting increasing concerns. The secure fingerprint identification has become one of the most important research topics because of these increasing concerns. It has promoted us to propose an efficient fingerprint identification algorithm based on minutiae and invariant moment in this paper. In the proposed algorithm, the raw fingerprint image is first enhanced by the short-time Fourier transform (STFT). After that, the fingerprint minutiae can be extracted, which thereafter are selected as the center of region of interest (ROI), according to morphological transformation. Finally, a metric called cosine similarity among invariant moments is utilized to judge the similarities between fingerprint objects in the procedure of identification. The proposed algorithm is not limited to the fingerprint image with the fingerprint core. Experimental results show that the proposed algorithm provides a better performance in terms of matching accuracy when compared with the related works. The average accuracy is up to 96.67%. Moreover, the use of invariant moment of the ROI can avoid the leakage of fingerprint information and improve the security level of fingerprint recognition. Therefore, the proposed scheme is potentially used in many applications, such as smart fingerprint lock, intelligent community management information system, and automation control of home appliances.

An efficient fingerprint identification algorithm based on minutiae and invariant moment

Cloud computing is a unique network or environment where access, maintenance and process can be done from any part of the world. It is a customized internet-based computer server. It is a current t...

Improving cloud data security through hybrid verification technique based on biometrics and encryption system

Fingerprint is one of the most important biometrics that has been employed for verification systems. Fingerprint is characterized by two fundamental properties; Easy to acquire, and it is unique for each person. This paper presents minutia extraction method based on Neural Network-based. These features can be used in verification systems. The verification process includes four main phases: image acquisition, preprocessing, feature extraction, and pattern matching. The method is applied on a set of fingerprint images and the results show that the average matching accuracy of fingerprints is 91.6%.

Neural network-based minutiae extraction for fingerprint verification system

A talking ATM may be a kind of Asynchronous Transfer Mode (ATM) that gives perceptible directions so that persons United Nations agency will not scan associate degree ATM screen can severally use the machine. All perceptible data is delivered in private through an earpiece jack on the face of the device or an on an individual basis hooked up a telephone. Information provided to the client either through pre-recorded sound files or via text-to-speech synthesis A user plugs a conventional telephone receiver into the jack, hear directions and also the user can respond to voice. In spite of everything, the small print is given press OK button for confirmation. There's associate degree perceptible orientation for initial time users, and perceptible data describing the placement of options like the OK button, deposit slot, and card slot. During this application is principally developed for the blind individuals. The blind people cannot see the keyboard and also the screen for the system. They enter the input details through voice. The voice to text converter can convert into text and method additional way. The system offers the instruction regarding the system usage through voice.

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Interaction With ATM for Blind

Fingerprint Recognition is one of the oldest and popular methods for person identification. There are two major approaches of fingerprint recognition namely image based and minutiae based. This paper presents an image based fingerprint recognition method. In this work first core point is detected and pre processing of image is done then transformation technique like Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Gabor Filter are used for feature extraction. These features consist of mean energy, standard deviation and Shannon entropy. The good recognition accuracy is achieved on the FVC 2002 database. Comparison of all the four transform is presented here and it is observed that DCT and DFT gives better result as compared to DWT and Gabor filter.

Fingerprint Recognition and feature extraction using transform domain techniques

Multibiometric systems are expected to be more reliable than unimodal biometric systems for personal identification due to the presence of multiple, fairly independent pieces of evidence e.g. Unique Identification Project "Aadhaar" of Government of India. In this paper, we present a novel wavelet based technique to perform fusion at the feature level and score level by considering two biometric modalities, face and fingerprint. The results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance. The proposed technique is simple because of no preprocessing of raw biometric traits as well as no feature and score normalization.

Kavita Tewari

Papers

Fingerprint Recognition and feature extraction using transform domain techniques

Simplified Multimodal Biometric Identification