Introduction

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

doi:10.1007/978-3-319-67526-8_1

Cited by 4 publications

(3 citation statements)

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“…The dataset contains a set of records (aka., instances, objects, or entities), where each record includes a set of attributes (aka., characteristics or features), as pointed out by Chandola et al [3]. In general, an anomaly detection method is provided with a record/set of records as an input, where no information about either anomalies or regular classes is known to the detection method in advance [27]. The three modes of anomaly detection methods, according to Chandola et al [3] -Does not care about the used PAI during the attack.…”

Section: Anomaly Detectionmentioning

confidence: 99%

A Survey on Unknown Presentation Attack Detection for Fingerprint

Singh¹,

Madhun²,

Li³

et al. 2020

Preprint

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Fingerprint recognition systems are widely deployed in various reallife applications as they have achieved high accuracy. The widely used applications include border control, automated teller machine (ATM), and attendance monitoring systems. However, these critical systems are prone to spoofing attacks (a.k.a presentation attacks (PA)). PA for fingerprint can be performed by presenting gummy fingers made from different materials such as silicone, gelatine, play-doh, ecoflex, 2D printed paper, 3D printed material, or latex. Biometrics Researchers have developed Presentation Attack Detection (PAD) methods as a countermeasure to PA. PAD is usually done by training a machine learning classifier for known attacks for a given dataset, and they achieve high accuracy in this task. However, generalizing to unknown attacks is an essential problem from applicability to real-world systems, mainly because attacks cannot be exhaustively listed in advance. In this survey paper, we present a comprehensive survey on existing PAD algorithms for fingerprint recognition systems, specifically from the standpoint of detecting unknown PAD. We categorize PAD algorithms, point out their advantages/disadvantages, and future directions for this area.

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Section: Anomaly Detectionmentioning

confidence: 99%

A Survey on Unknown Presentation Attack Detection for Fingerprint

Singh¹,

Madhun²,

Li³

et al. 2020

Preprint

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“…Outlier analysis offers a more efficient and streamlined alternative for identifying unique or unusual clinical observations. Specifically, it can identify an unusual observation that does not adhere to an expected behaviour; [35,36] that is, an observation which differs substantially from other observations, leading to suspicions that it originated from a distinct mechanism [37]. In biostatistics, outliers are conventionally considered to be statistical noise and are, therefore, often excluded from analyses [38].…”

Section: Introductionmentioning

confidence: 99%

“…However, distinguishing between statistical noise and an informative outlier and understanding the mechanisms giving rise to the latter may expose important and valuable information. Such an approach is now used in fields outside of medicine, including financial fraud detection, network connection anomalies, malware detection, and quality control in manufacturing processes [35,36]. Within the field of medicine, outlier analysis has been recently reported for the purposes of disease diagnosis, data quality assurance, and medication error screening, as well as for monitoring a patient's vital signs and then alerting a caregiver when those physiological measures considerably deviate beyond the normal parameters [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review

Janoudi,

Uzun (Rada),

Fell

et al. 2024

PLOS Digit Health

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Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.

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A Survey on Unknown Presentation Attack Detection for Fingerprint

Singh

Madhun

et al. 2021

Communications in Computer and Information Science

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Introduction

Cited by 4 publications

References 0 publications

A Survey on Unknown Presentation Attack Detection for Fingerprint

A Survey on Unknown Presentation Attack Detection for Fingerprint

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review

A Survey on Unknown Presentation Attack Detection for Fingerprint

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