Soft biometrics—combining body weight and fat measurements with fingerprint biometrics

Ailisto, Heikki; Vildjiounaite, Elena; Lindholm, Mikko; Mäkelä, Satu-Marja; Peltola, Johannes

doi:10.1016/j.patrec.2005.08.018

Cited by 69 publications

(33 citation statements)

References 18 publications

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“…Increasing the number of soft and primary biometric traits increases the uniqueness of a user's signature, leading to better discrimination between subjects. Ailisto et al (2006) obtained similar success using body weight and fat measurements to improve fingerprint recognition, reducing the total error rate of 62 test subjects from 3.9% to 1.5%. One concern, however, is the need for an automated technique to weight the soft biometric traits (Jain et al, 2004a).…”

Section: S0015mentioning

confidence: 73%

Soft Biometrics for Surveillance: An Overview

Reid

Samangooei

Chen

et al. 2013

Handbook of Statistics - Machine Learning: Theory and Applications

106

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sp005Biometrics is the science of automatically recognizing people based on physical or behavioral characteristics such as face, fingerprint, iris, hand, voice, gait, and signature. More recently, the use of soft biometric traits has been proposed to improve the performance of traditional biometric systems and allow identification based on human descriptions. Soft biometric traits include characteristics such as height, weight, body geometry, scars, marks, and tattoos (SMT), gender, etc. These traits offer several advantages over traditional biometric techniques. Soft biometric traits can be typically described using human understandable labels and measurements, allowing for retrieval and recognition solely based on verbal descriptions. Unlike many primary biometric traits, soft biometrics can be obtained at a distance without subject cooperation and from low quality video footage, making them ideal for use in surveillance applications. This chapter will introduce the current state of the art in the emerging field of soft biometrics.

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Section: S0015mentioning

confidence: 73%

Soft Biometrics for Surveillance: An Overview

Reid

Samangooei

Chen

et al. 2013

Handbook of Statistics - Machine Learning: Theory and Applications

106

View full text Add to dashboard Cite

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“…Ailisto Heikki proposed the usage of body weight and body fat percentage with finger print bio metrics for personal identification. The method uses optical fingerprint sensor Bio metrika FX2000 with FX3 SDK software development kit for identify ing a person [35] . Atsushi Sugiura used Fingerprint User Interface (FUI) for fingerprint recognition.…”

Section: Devices Used For Fingerprint Recognitionmentioning

confidence: 99%

Fingerprints, Iris and DNA Features based Multimodal Systems: A Review

Srivastava¹,

Agrawal²,

Mishra³

et al. 2013

IJITCS

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-Bio met ric systems are alternates to the traditional identification systems. This paper p rovides an overview of single feature and mult iple features based biometric systems, including the performance of physiological characteristics (such as fingerprint, hand geometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, finger veins, palates etc) and behavioral characteristics (such as body language, facial exp ression, signature verification, speech recognition, Gait Signature etc.).The fingerprints, iris image, and DNA features based mu ltimodal systems and their perfo rmances are analyzed in terms of security, reliab ility, accuracy, and long-term stability. The strengths and weaknesses of various mult iple features based biometric approaches published so far are analyzed. The d irect ions of future research work for robust personal identification is outlined.

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“…Most of the work in this area is geared toward biometric identification, where research has demonstrated that measuring these soft traits can improve the performance of biometric identification systems [2,9,14]. But the connection to work in biometrics is not critical, since information about these -soft‖ traits can be used for other purposes-which is part of our motivation for this work.…”

Section: Related Workmentioning

confidence: 99%

“…Soft biometric traits include the three traits that we predict in this paper-sex, height, and weight-which clearly are less distinctive or invariant than hard biometric traits like fingerprints. Nonetheless, these -soft‖ traits can be used in conjunction with other information or other traits to improve the accuracy of a biometric identification system [2,9,14]. While the work described in this paper can be used for biometric identification, our goal is far more general-we want to learn as much as possible about people (in this case smart phone users).…”

Section: Introductionmentioning

confidence: 99%

Identifying user traits by mining smart phone accelerometer data

Weiss

Lockhart

2011

Proceedings of the Fifth International Workshop on Knowledge Discovery From Sensor Data

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Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as -soft biometrics‖ because these traits are not sufficiently distinctive or invariant to uniquely identify an individual-but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user's weight when calculating calories burned).

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Soft biometrics—combining body weight and fat measurements with fingerprint biometrics

Cited by 69 publications

References 18 publications

Soft Biometrics for Surveillance: An Overview

Soft Biometrics for Surveillance: An Overview

Fingerprints, Iris and DNA Features based Multimodal Systems: A Review

Identifying user traits by mining smart phone accelerometer data

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