An Efficient Indexing Scheme Based on K-Plet Representation for Fingerprint Database

Bai, Chaochao; Zhao, Tong; Wang, Weiqiang; Wu, Min

doi:10.1007/978-3-319-22180-9_25

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Minutiae-based descriptors [36], [37] in the literature include minutiae singles [38], [39], minutiae pairs [29], minutiae triplets using Delaunay triangulation [28], [40], convex structures [41], minutiae k-plets [12], [26], and minutia cylinder codes (MCC) [14], [15]. Minutia cylinder codes [42] have gained increasing attention in the recent years [16]- [18].…”

Section: A Minutia Cylinder Codes (Mcc)mentioning

confidence: 99%

“…For la-tent fingerprint identification, the query should be compared against those impressions that, according to their feature vector, are close one another [11]. Many descriptors and techniques have been used by indexing algorithms [12], [28], [31], [38], [42], [44]. Among them, we have indexing based on spatial data structures, singular points, ridge-line frequency, Galton-Henry classification scheme, and minutiaebased descriptors [11].…”

Section: B Indexing For Latent Fingerprintmentioning

confidence: 99%

“…This is because they decay identification accuracy or because they are not up to a fingerprint database of even a moderate size. This could be explained by the fact that most indexing mechanism have been designed and tested considering only the fingerprint verification (see, for example, [12]- [22]); further, they have been tested using rather small databases (see, for example, [23]- [29]. )…”

Section: Introductionmentioning

confidence: 99%

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An Indexing Algorithm Based on Clustering of Minutia Cylinder Codes for Fast Latent Fingerprint Identification

et al. 2021

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Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates' order to be compared, fingerprint indexing algorithms that reduce the search space while minimizing the increase in the error rate (compared to the identification) are developed. In the present research, we propose an algorithm for indexing latent fingerprints based on minutia cylinder codes (MCC) . This type of minutiae descriptor presents a fixed structure, which brings advantages in terms of efficiency. Besides, in recent studies, this descriptor has shown an identification error rate, at the local level, lower than the other descriptors reported in the literature. Our indexing proposal requires an initial step to construct the indices, in which it uses k-means++ clustering algorithm to create groups of similar minutia cylinder codes corresponding to the impressions of a set of databases. K-means++ allows for a better outcome over other clustering algorithms because of the selection of the proper centroids. The buckets associated with each index are populated with the background databases. Then, given a latent fingerprint, the algorithm extracts the minutia cylinder codes associated with the clusters' indices with the lowest distance respect to each descriptor of this latent fingerprint. Finally, it integrates the votes represented by the fingerprints obtained to select the candidate impressions. We conduct a set of experiments in which our proposal outperforms current rival algorithms in presence of different databases and descriptors. Also, the primary experiment reduces the search space by four orders of magnitude when the background database contains more than one million impressions.INDEX TERMS Fingerprint indexing, Latent fingerprint, K-means Clustering, Minutia Cylinder Code.

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Section: A Minutia Cylinder Codes (Mcc)mentioning

confidence: 99%

Section: B Indexing For Latent Fingerprintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Indexing Algorithm Based on Clustering of Minutia Cylinder Codes for Fast Latent Fingerprint Identification

et al. 2021

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“…Kumar et al [24] proposed to add some undefined features to minutiae. Mansukhani et al [32] and Bai et al [4] proposed to use trees for fast comparison of local minutia neighbourhoods. Hartloff et al [14] used series of neighbouring minutiae and concatenated them into sequences (see Fig.…”

Section: Approaches Using Fp Minutiaementioning

confidence: 99%

“…[32] and Bai et al . [4] proposed to use trees for fast comparison of local minutia neighbourhoods. Hartloff et al .…”

Section: Relevant Approachesmentioning

confidence: 99%

Survey on features for fingerprint indexing

Schuch¹

2018

IET biom.

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Nowadays, several biometric databases already contain millions of entries of individuals. With an increasing number of enrolled individuals, the response time of queries grows and can become critical. Fingerprint indexing offers a set of techniques to reduce the workload of entries, which have to be compared thoroughly. This work surveys research on such techniques. It focuses on the features of fingerprints, which are used as input. This survey also provides an assessment of the quality of the body of research in this field. Deficiencies herein are identified, e.g. there is a lack of common datasets and metrics used for testing.

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