Understanding visual lip-based biometric authentication for mobile devices

Wright, Carrie; Stewart, Darryl

doi:10.1186/s13635-020-0102-6

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…Wright et al created two datasets that are available upon request [125]. The first dataset is qFace.…”

Section: Qface and Favlipsmentioning

confidence: 99%

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Data-Driven Advancements in Lip Motion Analysis: A Review

Torrie,

Sumsion,

Lee

et al. 2023

Electronics

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This work reviews the dataset-driven advancements that have occurred in the area of lip motion analysis, particularly visual lip-reading and visual lip motion authentication, in the deep learning era. We provide an analysis of datasets and their usage, creation, and associated challenges. Future research can utilize this work as a guide for selecting appropriate datasets and as a source of insights for creating new and innovative datasets. Large and varied datasets are vital to a successful deep learning system. There have been many incredible advancements made in these fields due to larger datasets. There are indications that even larger, more varied datasets would result in further improvement upon existing systems. We highlight the datasets that brought about the progression in lip-reading systems from digit- to word-level lip-reading, and then from word- to sentence-level lip-reading. Through an in-depth analysis of lip-reading system results, we show that datasets with large amounts of diversity increase results immensely. We then discuss the next step for lip-reading systems to move from sentence- to dialogue-level lip-reading and emphasize that new datasets are required to make this transition possible. We then explore lip motion authentication datasets. While lip motion authentication has been well researched, it is not very unified on a particular implementation, and there is no benchmark dataset to compare the various methods. As was seen in the lip-reading analysis, large, diverse datasets are required to evaluate the robustness and accuracy of new methods attempted by researchers. These large datasets have pushed the work in the visual lip-reading realm. Due to the lack of large, diverse, and publicly accessible datasets, visual lip motion authentication research has struggled to validate results and real-world applications. A new benchmark dataset is required to unify the studies in this area such that they can be compared to previous methods as well as validate new methods more effectively.

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“…Wright et al created two datasets that are available upon request [125]. The first dataset is qFace.…”

Section: Qface and Favlipsmentioning

confidence: 99%

“…The systems [125,[131][132][133][134] that utilize the XM2VTS dataset compare individuals speaking the same phrases and distinguish between them by determining how each individual utters the same phrases. Similar approaches are often used by other systems as well.…”

Section: Faraj Et Al Utilizes Lip Motion As a Form Of Liveness Detectionmentioning

confidence: 99%

Data-Driven Advancements in Lip Motion Analysis: A Review

Torrie,

Sumsion,

Lee

et al. 2023

Electronics

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“…Cruz et al [13] used combined Lip based biometric with Viola Jones and appearance based model (AAM) system. Wright and Stewart [14] presented visual lip-based biometric authentication system. Farrukh et al [15] used deep learning based methods for lop print identification.…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization

Krishnamoorthy,

Raju

2024

IJEECS

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establishes a robust authentication system, countering vulnerabilities in unimodal methods. This approach leverages ECG's robustness against spoofing attacks and lip's fine-grained behavioral cues for comprehensive authentication. Ensemble learning techniques, from majority voting to advanced methods, harness the strengths of individual models, improving accuracy, reliability, and generalization. Moreover, ensemble learning detects anomalies, enhancing security. The study incorporates ECG signal filtering and lip region extraction as preprocessing, uses wavelet transform for ECG features, SIFT for lip image features, and employs greywolf optimization for feature selection. Ultimately, a voting-based ensemble classifier is applied for classification, showcasing the potential of this integrated approach in fortified security measures.

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Section: Introductionmentioning

confidence: 99%

“…[10] Lip reading technology, which is also commonly known as visual speech recognition, has other advantages, such as ensuring the security of HMC with or without the audio signal, maintaining a noise-free environment, and biometric authentication. [11] Therefore, lip-reading technology is considered as a possible candidate for multimodal HMCs.However, lip-reading technology works well only in a welllit environment. Poor illumination can affect feature extraction and the deep learning of lip movements, which consequently affects the accuracy of the model.…”

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confidence: 99%

Dark‐Mode Human–Machine Communication Realized by Persistent Luminescence and Deep Learning

Timilsina

Shin

Sohn

et al. 2022

Advanced Intelligent Systems

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Increasing ubiquitous collaborative intelligence between humans and machines requires human–machine communication (HMC) that is more human and less machine‐like to accomplish given tasks. Although speech signals are considered the best modes of communication in HMC, background noise often interferes with these signals. Therefore, research focused on integrating lip‐reading technology into HMC has gained significant attention. However, lip‐reading functions effectively only in well‐lit environments. In contrast, HMC may occur daily in dark environments owing to potential energy shortages, increased exploration in darkness, nighttime emergencies, etc. Herein, a possible method for HMC in the dark mode is presented, which is realized based on deep learning motion patterns of persistent luminescence (PL) of the skin surrounding the lips. An ultrasoft PL–polymer composite patch is used to record the motion pattern of the skin during speech in the dark. It is found that visual geometric group network (VGGNET‐5) and residual neural network (ResNet‐34) could predict spoken words in darkness with test accuracies of 98.5% and 98.75%, respectively. Furthermore, these models could effectively distinguish similar‐sounding words such as “around” and “ground.” Dark‐mode communication can allow a wide range of people, including disabled people with limited dexterity and voice tremors, to communicate with artificial intelligence machines.

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Understanding visual lip-based biometric authentication for mobile devices

Cited by 11 publications

References 20 publications

Data-Driven Advancements in Lip Motion Analysis: A Review

Data-Driven Advancements in Lip Motion Analysis: A Review

An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization

Dark‐Mode Human–Machine Communication Realized by Persistent Luminescence and Deep Learning

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