An Efficient Fingerprint Matching Approach Based on Minutiae to Minutiae Distance Using Indexing with Effectively Lower Time Complexity

Tuberculosis (TB) is one of the infectious diseases spread by the infectious agent Mycobacterium tuberculosis. Sputum smear microscopy is the primary tool used for the diagnosis of pulmonary TB, but has its limitations such as low sensitivity and large observation time. Hence, an automated technique is preferred for the diagnosis of TB. This paper develops a technique for TB diagnosis based on the bacilli count by proposing Fuzzy and Hyco-entropy-based Decision Tree (FHDT) classifier using sputum smear microscopic images. The proposed technique involves three steps: segmentation, feature extraction and classification. Initially, the input sputum smear microscopic image is subjected to a colour space transformation, for which a thresholding is applied to obtain the segmented result. Important features such as length, density, area and few histogram features are extracted for FHDT-based classification that classifies the segments into few-bacilli, non-bacilli and overlapping bacilli. An entropy function, called hyco-entropy, is designed for the optimal selection of feature. For further analysis of classification, that is, to count the number in the overlapping bacilli, the fuzzy classifier is adopted. FHDT classifier is evaluated in terms of Segmentation Accuracy (SA), Mean Squared Error (MSE) and Missing Count (MC) using microscopic images taken from ZNSM-iDB, where it can attain maximum mean SA of 0.954 and mean MC of 2.4.

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“…where M y is the match degree with the fuzzy set F and is formulated with respect to a membership degree as in Eq. (14).…”

Section: Proposed Fuzzy and Hyco-entropy-based Decision Tree For The mentioning

confidence: 99%

“…Therefore, it is necessary to develop an automatic diagnostic process to enhance the sensitivity and the accuracy of the test. Pattern-recognition [14,15] and image-processing methods are the promising tools employed for automatic screening of TB sputum smear images [16].…”

Section: Introductionmentioning

confidence: 99%

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Mithra

Emmanuel

2018

Sādhanā

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“…The benefit of this method is that it is programmed on the same hardware architecture following a software design flow. Barman et al [28] developed a method of fingerprintbased authentication system, in which fingerprint matching was performed using the distance of minutiae points only. This method was straightforward and it required very little space to store templates.…”

Section: Related Workmentioning

confidence: 99%

GwPeSOA-based MSVNN: the multimodal biometric system for futuristic security applications

Vijay¹,

Indumathi²

2018

Sādhanā

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Biometric systems have gained considerable significance as they are highly employed in the security applications. Achieving human recognition is easier and cheaper and the single modality employed for the recognition faces a lot of challenges due to the environmental factors. Thus, the paper proposes a multimodal recognition system based on the Multi-Support Vector Neural Network (MSVNN). The algorithm proposed is the Glowworm Penguin Search Optimization Algorithm (GwPeSOA), which is a modification of the Glowworm Optimization Algorithm (GOA) with the Penguin Search Optimization Algorithm (PeSOA). The proposed method employs two modalities: the ear and the finger vein modalities; the features of the ear image are obtained using the proposed BiComp masking method of feature extraction, whereas the features from the finger vein are extracted using the Repeated Line Tracking (RLT) method. The features obtained are applied to the MSVNN classifier to recognize the person with good accuracy and the proposed BiComp Mask offers robust features for the extraction. The experimentation using the proposed method attained a better accuracy, specificity and sensitivity at a rate of 0.95, 0.95 and 0.9868, respectively.

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“…These local features are assumed to be mainly consistent as illustrated in [6][7][8]. A computationally uncomplicated fingerprint verification system based on only location of minutiae points in an image is discussed in [9]. Chu and Chiu [10] encoded neighbourhood structure of each minutiae using a new disk structure.…”

Section: Related Workmentioning

confidence: 99%

Rotation-invariant fingerprint matching using radon and DCT

Bharkad

Kokare

2017

Sādhanā

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A new set of promising rotation-invariant features based on radon and discrete cosine transform (DCT) is proposed for fingerprint matching. The radon and DCT of a tiny area in the region of core point of fingerprint image is computed. In the proposed method only 34% DCT coefficients are used for feature extraction. Competency of this approach is tested on standard databases, namely FVC2002 and FVC2004. This approach provides 70% genuine acceptance rate (GAR) at *0% false acceptance rate (FAR) and 95% GAR at 10% FAR on rotated and non-rotated databases, respectively. Experimental results prove that the proposed feature extraction approach is rotation invariant.

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An Efficient Fingerprint Matching Approach Based on Minutiae to Minutiae Distance Using Indexing with Effectively Lower Time Complexity

Cited by 20 publications

References 7 publications

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

GwPeSOA-based MSVNN: the multimodal biometric system for futuristic security applications

Rotation-invariant fingerprint matching using radon and DCT

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