Integration of multiple soft biometrics for human identification

Arigbabu, Olasimbo Ayodeji; Ahmad, Sharifah Mumtazah Syed; Adnan, Wan Azizun Wan; Yussof, Salman

doi:10.1016/j.patrec.2015.07.014

Cited by 28 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fusion of two or more biometric modalities has been advocated as an effective method for increasing the overall performance of a biometric system [12][13]. Significant improvement in the overall accuracy of a fingerprint based system is reported in literature [16,37,38] when combined with other modalities.…”

Section: Multimodal Systemmentioning

confidence: 99%

“…Furthermore, typical multi-biometric systems require the user to provide separate data for each modality (face, palm-print, iris etc.) [13,16,28,29], making the process lengthy and cumbersome. The scheme proposed in this paper (see Fig.…”

Section: Multimodal Systemmentioning

confidence: 99%

“…Even the well-established biometric techniques, like fingerprint, may require human compliance and are argued to be obtrusive and stigmatic by many researchers [12,13]. Some prime challenges include (a) problem of high intra class variability, (b) environmental conditions, (c) failure to enroll, (d) attacks, and (e) data acquisition issues like noise, rotation, displacement etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Bhardwaj

Londhe

Kopparapu³

2017

Pattern Recognition

View full text Add to dashboard Cite

Section: Multimodal Systemmentioning

confidence: 99%

Section: Multimodal Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Bhardwaj

Londhe

Kopparapu³

2017

Pattern Recognition

View full text Add to dashboard Cite

“…Combining several soft biometrics modalities, especially clothing, has proven important in improving subject recognition rates [3,14] and can be estimated for surveillance tracking and search [9,44].…”

Section: Pedestrian Re-identificationmentioning

confidence: 99%

Analysing comparative soft biometrics from crowdsourced annotations

2016

View full text Add to dashboard Cite

Soft biometrics enable human description and identification from low quality surveillance footage. This paper premises the design, collection and analysis of a novel crowdsourced dataset of comparative soft biometric body annotations, obtained from a richly diverse set of human annotators. We annotate 100 subject images to provide a coherent, in-depth appraisal of the collected annotations and inferred relative labels. The dataset includes gender as a comparative trait and we find that comparative labels characteristically contain additional discriminative information over traditional categorical annotations. Using our pragmatic dataset, we perform semantic recognition by inferring relative biometric signatures using a RankSVM algorithm. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The approach can reliably return the correct match in the the top 7% of results with 10 comparisons, or top 13% of results using just 5 sets of subject comparisons.

show abstract

“…The various biometrics that are currently being adopted in identity recognition scenarios such as fingerprint, iris, face and speech recognition have operational trade-offs in terms of performance, measurability, circumvention and liveness detection [ 1 ]. These limitations have led to the development of smart solutions such as: (i) improving voice recognition by sophisticated pre-processing, followed by heuristical selection of significant features in time and frequency domains [ 2 ]; (ii) integrating face- and body-related soft biometric traits [ 3 ]; (iii) providing security and flexibility by simultaneous application of numeric password data and biometric fingerprint information [ 4 ]; (iv) assuring liveness detection via optical sensors, which establish the presence of pulse, the variations of optical characteristics caused by pressure changes and skin reaction to illumination with different wavelengths [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Human Identification by Cross-Correlation and Pattern Matching of Personalized Heartbeat: Influence of ECG Leads and Reference Database Size

Jekova

Krasteva

Schmid³

2018

Sensors

View full text Add to dashboard Cite

Human identification (ID) is a biometric task, comparing single input sample to many stored templates to identify an individual in a reference database. This paper aims to present the perspectives of personalized heartbeat pattern for reliable ECG-based identification. The investigations are using a database with 460 pairs of 12-lead resting electrocardiograms (ECG) with 10-s durations recorded at time-instants T1 and T2 > T1 + 1 year. Intra-subject long-term ECG stability and inter-subject variability of personalized PQRST (500 ms) and QRS (100 ms) patterns is quantified via cross-correlation, amplitude ratio and pattern matching between T1 and T2 using 7 features × 12-leads. Single and multi-lead ID models are trained on the first 230 ECG pairs. Their validation on 10, 20, ... 230 reference subjects (RS) from the remaining 230 ECG pairs shows: (i) two best single-lead ID models using lead II for a small population RS = (10–140) with identification accuracy AccID = (89.4–67.2)% and aVF for a large population RS = (140–230) with AccID = (67.2–63.9)%; (ii) better performance of the 6-lead limb vs. the 6-lead chest ID model—(91.4–76.1)% vs. (90.9–70)% for RS = (10–230); (iii) best performance of the 12-lead ID model—(98.4–87.4)% for RS = (10–230). The tolerable reference database size, keeping AccID > 80%, is RS = 30 in the single-lead ID scenario (II); RS = 50 (6 chest leads); RS = 100 (6 limb leads), RS > 230—maximal population in this study (12-lead ECG).

show abstract

Integration of multiple soft biometrics for human identification

Cited by 28 publications

References 26 publications

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Analysing comparative soft biometrics from crowdsourced annotations

Human Identification by Cross-Correlation and Pattern Matching of Personalized Heartbeat: Influence of ECG Leads and Reference Database Size

Contact Info

Product

Resources

About