A Proposed Approach for Biometric-Based Authentication Using of Face and Facial Expression Recognition

Yin, Delina Beh Mei; Mukhlas, Amalia-Amelia; Wan-Chik, Rita Zaharah; Othman, Abu Talib; Omar, Shariman

doi:10.1109/icomis.2018.8644974

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Facial expressions can also serve as a novel input method, a concept has been explored in previous research efforts [30,43,44,51]. Our glasses-based system is well-suited for implementing this functionality, allowing different facial expressions to serve as distinct commands for interacting with smart or augmented reality glasses.…”

Section: Applications On Commodity Devicesmentioning

confidence: 99%

EyeEcho: Continuous and Low-power Facial Expression Tracking on Glasses

Li,

Zhang,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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In this paper, we introduce EyeEcho, a minimally-obtrusive acoustic sensing system designed to enable glasses to continuously monitor facial expressions. It utilizes two pairs of speakers and microphones mounted on glasses, to emit encoded inaudible acoustic signals directed towards the face, capturing subtle skin deformations associated with facial expressions. The reflected signals are processed through a customized machine-learning pipeline to estimate full facial movements. EyeEcho samples at 83.3 Hz with a relatively low power consumption of 167𝑚𝑊 . Our user study involving 12 participants demonstrates that, with just four minutes of training data, EyeEcho achieves highly accurate tracking performance across different real-world scenarios, including sitting, walking, and after remounting the devices. Additionally, a semi-in-the-wild study involving 10 participants further validates EyeEcho's performance in naturalistic scenarios while participants engage in various daily activities. Finally, we showcase EyeEcho's potential to be deployed on a commercial-off-the-shelf (COTS) smartphone, offering real-time facial expression tracking. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile devices; • Hardware → Power and energy.

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Section: Applications On Commodity Devicesmentioning

confidence: 99%

EyeEcho: Continuous and Low-power Facial Expression Tracking on Glasses

Li,

Zhang,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…Our work is not the first demonstration of using facial motion verification to enhance the biometric identification system. In 2018, Yin et al [4] proposed a method that evaluates facial appearance and expression to verify the user's identity. This work focused on static facial images.…”

Section: Facial Motion and Identity Verificationmentioning

confidence: 99%

“…However, spoofing a biometric identification system using 3D molds or pictures with distinct textures [3] is still possible. Some recent research studies [4,5] have introduced a new concept that uses facial motion as a passcode while confirming the user's regular facial appearance. The corresponding solutions increase the security of identification procedures by requiring the candidates to present the exact biometric trait together with a correct enrolled facial motion.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the decision function compares these descriptions with the recorded ones to reject or grant access to the system. Previous work [4,5] has proven that deep neural network models can generate accurate representations for predefined facial expressions or one-off freestyle facial motions. In this work, we studied compound facial actions, which contain more than one significant facial action in one utterance.…”

Section: Introductionmentioning

confidence: 99%

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Self-Supervised Facial Motion Representation Learning via Contrastive Subclips

et al. 2023

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Facial motion representation learning has become an exciting research topic, since biometric technologies are becoming more common in our daily lives. One of its applications is identity verification. After recording a dynamic facial motion video for enrollment, the user needs to show a matched facial appearance and make a facial motion the same as the enrollment for authentication. Some recent research papers have discussed the benefits of this new biometric technology and reported promising results for both static and dynamic facial motion verification tasks. Our work extends the existing approaches and introduces compound facial actions, which contain more than one dominant facial action in one utterance. We propose a new self-supervised pretraining method called contrastive subclips that improves the model performance with these more complex and secure facial motions. The experimental results show that the contrastive subclips method improves upon the baseline approaches, and the model performance for test data can reach 89.7% average precision.

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“…Another strategy for biometric identi cation combines the ear and nger knuckle print because the ear biometric is stable from birth and thus unique [24]. The authors propose combining the physiological and behavioral aspects of the face, speci cally the face and facial expression, to lessen the susceptibility of unimodal biometric authentication [25]. The technology uses facial expressions to identify an authorized user.…”

Section: Related Workmentioning

confidence: 99%

Compression-Based Cancelable Multi-Biometric System

Elshazly

Hashad

Sedik

et al. 2022

Preprint

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The issue of cybersecurity is one of the important fields which is involved in different research trends. Biometric security is one of these trends which is involved in several applications such as access control systems and online identity verification. The protection of human biometrics can be performed using both bi-directional and unidirectional encryption. The unidirectional encryption is carried out based on cancelable biometric techniques. This paper proposes a cancelable biometric system based on image composition, deep dream, and hashing techniques. The objective of the proposed system is to generate visual and text cancelable biometrics. The visual cancelable templates are generated using image composition and deep dream, while the text templates are generated using SHA hashing techniques. The proposed system is validated by multi-biometric inputs including iris, palm, face, and fingerprint biometrics. In addition, it is evaluated in both visual and text forms. The simulation results reveal that the proposed system appears a superior performance among the works which handle this problem.

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A Proposed Approach for Biometric-Based Authentication Using of Face and Facial Expression Recognition

Cited by 13 publications

References 27 publications

EyeEcho: Continuous and Low-power Facial Expression Tracking on Glasses

EyeEcho: Continuous and Low-power Facial Expression Tracking on Glasses

Self-Supervised Facial Motion Representation Learning via Contrastive Subclips

Compression-Based Cancelable Multi-Biometric System

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