Smartphone Multi-modal Biometric Authentication: Database and Evaluation

Ramachandra, Raghavendra; Stokkenes, Martin; Mohammadi, Amir; Venkatesh, Sushma; Raja, Kiran B.; Wasnik, Pankaj; Poiret, Eric; Marcel, Sébastien; Busch, Christoph

doi:10.48550/arxiv.1912.02487

Cited by 7 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis on the solutions: Table 15 summarizes the FAS solutions of the six teams. The teams SiMiT Lab and NTNU Gjøvik utilized several public datasets for training, including the 2D PAIs (e.g., Replay-Mobile [10], SiW [48], Oulu-NPU [4], SWAN [58], CASIA-FASD [93]) and 3D mask attacks (e.g., 3DMAD [17] and NTNU-Silicon Mask [59]) datasets. The team Fraunhofer IGD improved the generalization of their algorithm by using the 50 attacks and bona fide samples from the Real Mask Attack Database (CRMA) [18] as unknown development data to tune the decision threshold.…”

Section: Resultsmentioning

confidence: 99%

Review of Face Presentation Attack Detection Competitions

Yu¹,

Komulainen²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

Face presentation attack detection (PAD) has received increasing attention ever since the vulnerabilities to spoofing have been widely recognized. The state of the art in unimodal and multi-modal face anti-spoofing has been assessed in eight international competitions organized in conjunction with major biometrics and computer vision conferences in 2011, 2013, 2017, 2019, 2020 and 2021, each introducing new challenges to the research community. In this chapter, we present the design and results of the five latest competitions from 2019 until 2021. The first two challenges aimed to evaluate the effectiveness of face PAD in multi-modal setup introducing near-infrared (NIR) and depth modalities in addition to colour camera data, while the latest three competitions focused on evaluating domain and attack type generalization abilities of face PAD algorithms operating on conventional colour images and videos. We also discuss the lessons learnt from the competitions and future challenges in the field in general.

show abstract

Section: Resultsmentioning

confidence: 99%

Review of Face Presentation Attack Detection Competitions

Yu¹,

Komulainen²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The sensors used in this work are the rear camera of the Asus Transformer Pad TF 300T. The Smartphone Multimodal Biometric database was collected for the application of mobile banking [30]. The realworld scenarios are attributed in this database with multiple sessions and different languages using iPhone 6s and iPad Pro.…”

Section: Related Workmentioning

confidence: 99%

“…The face recognition PAD methods are chosen from the baseline methods used in smartphone dataset evaluation in [30]. The two best-performing methods from five baseline methods are taken for evaluation in this work.…”

Section: ) Face Padmentioning

confidence: 99%

“…The language difference in the audio sample for ASV has been a hot topic in recent years. Although there are datasets with utterances of the same person in different languages, the problem of language dependency is not benchmarked [30].…”

Section: ) Inter-language Speaker Recognitionmentioning

confidence: 99%

“…In this direction, to examine the challenges, we need a smartphone biometrics database with different attributes. There are few biometric databases have been created using smartphones in both uni-modal [31] and multimodal biometrics [19], [30]. However, the existing databases are limited with number of devices, languages and sessions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multilingual Audio-Visual Smartphone Dataset And Evaluation

Mandalapu¹,

N²,

Ramachandra³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to the usability and also it will be challenging to spoof because of multi-modal nature. In this work, we present an audiovisual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems. These unique characteristics of this dataset will pave the way to implement novel state-of-the-art unimodal or audio-visual speaker recognition systems. We also report the performance of the bench-marked biometric verification systems on our dataset. The robustness of biometric algorithms is evaluated towards multiple dependencies like signal noise, device, language and presentation attacks like replay and synthesized signals with extensive experiments. The obtained results raised many concerns about the generalization properties of state-of-the-art biometrics methods in smartphones.

show abstract

Cross-lingual Speaker Verification: Evaluation on X-Vector Method

Mandalapu

Elbo

Ramachandra

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Automatic Speaker Verification (ASV) systems accuracy is based on the spoken language used in training and enrolling speakers. Language dependency makes voice-based security systems less robust and generalizable to a wide range of applications. In this work, a study on language dependency of a speaker verification system and experiments are performed to benchmark the robustness of the x-vector based techniques to language dependency. Experiments are carried out on a smartphone multi-lingual dataset with 50 subjects containing utterances in four different languages captured in five sessions. We have used two world training datasets, one with only one language and one with multiple languages. Results show that performance is degraded when there is a language mismatch in enrolling and testing. Further, our experimental results indicate that the performance degradation depends on the language present in the word training data.

show abstract

Smartphone Multi-modal Biometric Authentication: Database and Evaluation

Cited by 7 publications

References 28 publications

Review of Face Presentation Attack Detection Competitions

Review of Face Presentation Attack Detection Competitions

Multilingual Audio-Visual Smartphone Dataset And Evaluation

Cross-lingual Speaker Verification: Evaluation on X-Vector Method

Contact Info

Product

Resources

About