BioSoft - a multimodal biometric database incorporating soft traits

Sadhya, Debanjan; Pahariya, Parth; Yadav, Rishi; Rastogi, Apoorv; Kumar, Abhimanyu; Sharma, Lakshya; Singh, Sanjay Kumar

doi:10.1109/isba.2017.7947693

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…For example, to categorize individuals or compare them using height attribute will result in qualitative terms using categorical or comparative method. In soft biometrics research, a large number of datasets are annotated using stated two methods and using one or both types of annotations [173].…”

Section: Annotation Processes and Typesmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

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Section: Annotation Processes and Typesmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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“…However, collecting a diverse training set within a long-term real-world HRI scenario is very challenging. To the best of our knowledge, the only publicly available dataset that contains the soft biometrics used in our system (except for the time of interaction) with a dataset of faces is BioSoft [74]. However, due to the low number of subjects (75), and the lack of numeric height values, we decided to create our own Multi-modal Long-term User Recognition Dataset.…”

Section: Multi-modal Long-term User Recognition Datasetmentioning

confidence: 99%

Multi-modal Open World User Identification

Irfan

Ortiz

Lyubova

et al. 2021

J. Hum.-Robot Interact.

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User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.

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“…The face, hand, and iris attributes for the same person were included in our multimodal biometric database (MULBv1); these traits were not included simultaneously in other databases. Voice, 2D face [11] 2003 BANCA 202 12 2 2D face, voice [12] 2003 MYCT 330 1 2 Fingerprint, signature [13] 2005 MyIDEA 104 3 6 Voice, face, signature, fingerprints, hand geometry, handwriting [14] 2006 M3 32 3 3 Voice, 2D face, fingerprint [15] 2007 BioSec 250 4 4 Voice, 2D face, fingerprint, iris [16] 2008 IV 2 300 1 3 Iris, 2D and 3D face [17] 2011 SDUMLA-HMT 106 -5 Gait, iris, finger vein, 2D face [18] 2012 BIOMENT 91 3 2 2D face, fingerprint [19] 2015 DMCSv1 35 2 2 Hand, 3D face [20] 2017 The remainder of this paper is structured as follows: the properties of the MULBv1 database are fully explained in section 2. The case study for face recognition using a deep convolution neural network is shown in section 3 utilizing the face sub-database for the MULBv1 database.…”

Section: Introductionmentioning

confidence: 99%

A multimodal biometric database and case study for face recognition based deep learning

Kadhim,

Hasan Abdulameer

2024

Bulletin EEI

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Recently, multimodal biometric systems have garnered a lot of interest for the identification of human identity. The accessibility of the database is one of the contributing elements that impact biometric recognition systems. In their studies, the majority of researchers concentrate on unimodal databases. There was a need to compile a fresh, realistic multimodal biometric database, nonetheless, because there were so few comparable multimodal biometric databases that were publically accessible. This study introduces the MULBv1 multimodal biometric database, which contains homologous biometric traits. The MULBv1 database includes 20 images of each person's face in various poses, facial emotions, and accessories, 20 images of their right hand from various angles, and 20 images of their right iris from various lighting positions. The database contains real multimodal data from 174 people, and all biometrics were accurately collected using the micro camera of the iPhone 14 Pro Max. A face recognition technique is also suggested as a case study using the gathered facial features. In the case study, the deep convolutional neural network (CNN) was used, and the findings were positive. Through several trials, the accuracy was (97.41%).

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BioSoft - a multimodal biometric database incorporating soft traits

Cited by 9 publications

References 10 publications

Soft biometrics: a survey

Soft biometrics: a survey

Multi-modal Open World User Identification

A multimodal biometric database and case study for face recognition based deep learning

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