Fingerprint Recognition using Gabor wavelet in MapReduce and Spark

Phan, Anh-Cang; Tran, Ho-Dat; Phan, Thuong-Cang

doi:10.1145/3287921.3287934

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Specially, some edge-cloud applications in traditional DAG-based systems generate a lot of DAG jobs (e.g. distributed computing [4,18], face/fingerprint recognition [52,53], and big data classification [36,56,62]). The other edge-cloud applications for executing AI tasks generate a large number of AI jobs (e.g.…”

Section: Heterogeneous Workloadsmentioning

confidence: 99%

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Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

et al. 2023

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Edge-cloud applications are rapidly prevailing in recent years and pose the challenge of using both resource-strenuous edge devices and elastic cloud resources under dynamic workloads. Efficient resource allocation on edge-cloud jobs via cluster schedulers (e.g. Kubernetes/Volcano scheduler) is essential to guarantee their performance, e.g. tail latency, and such allocation is sensitive to scheduler configurations such as applied scheduling algorithms and task restart/discard policy. Deep reinforcement learning (DRL) is increasingly applied to optimize scheduling decisions. However, DRL faces the conundrum of achieving high rewards at a dauntingly long training time (e.g. hours or days), making it difficult to tune the scheduler configurations online in accordance to dynamically changing edge-cloud workloads and resources. For such an issue, this paper proposes EdgeTuner, a fast scheduler configuration tuning approach that efficiently leverages DRL to reduce tail latency of edge-cloud jobs. The enabling feature of EdgeTuner is to effectively simulate the execution of edge-cloud jobs under different scheduler configurations and thus quickly estimate these configurations’ influence on job performance. The simulation results allow EdgeTuner to timely train a DRL agent in order to properly tune scheduler configurations in dynamic edge-cloud environment. We implement EdgeTuner in both Kubernetes and Volcano schedulers and extensively evaluate it on real workloads driven by Alibaba production traces. Our results show that EdgeTuner outperforms prevailing scheduling algorithms by achieving much lower tail latency while accelerating DRL training speed by an average of 151.63x.

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Section: Heterogeneous Workloadsmentioning

confidence: 99%

“…• Directed acyclic graph (DAG) jobs, whose tasks have sequential dependence. Examples include distributed data processing [4,18,23], face/fingerprint recognition [52,53], and image classification [36,55,56,62]. In particular, MapReduce jobs are a representative type of DAG jobs.…”

Section: Introductionmentioning

confidence: 99%

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

et al. 2023

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“…Due to this, it is a primary choice for our feature selection. e Daubechies 9 (db9) wavelet is considered in this study as it generates similar results compared to complex Gabor wavelets [18]. It also extracts more appropriate features from an image relative to simpler wavelets such as Haar.…”

Section: Wavelet Transform Techniquementioning

confidence: 99%

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Appati

Nartey

Owusu

et al. 2021

International Journal of Mathematics and Mathematical Sciences

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Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.

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Noise attenuation and ridge processing technique for fingerprint bit minimization

Babatunde

Aladesaye

2021

Iran J Comput Sci

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Fingerprint Recognition using Gabor wavelet in MapReduce and Spark

Cited by 4 publications

References 5 publications

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Noise attenuation and ridge processing technique for fingerprint bit minimization

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