A novel method for real time face spoof recognition for single and multiple user authentication

Chinchu, S; Mohammed, Anisha; Mahesh, Balakrishnan

doi:10.1109/icicict1.2017.8342593

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Smartphones that store large amounts of private data are very popular, with 344.3 million sold worldwide in the first quarter of 2017 (1Q17) [1]- [3]. Normally, mobile devices are protected by static mechanisms like a personal identification number (PIN), fingerprint, and face recognition [4]- [7]. However, they are not entirely secure.…”

Section: Introductionmentioning

confidence: 99%

Improving Reliability: User Authentication on Smartphones Using Keystroke Biometrics

Wang

Zheng

et al. 2019

IEEE Access

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Keystroke biometrics is a well-investigated dynamic behavioral methodology that utilizes the unique behavioral patterns of users to verify their identity when tapping keys. However, the performance of keystroke biometrics is unreliable due to its high error rate and low robustness. In this paper, we propose differential evolution and adversarial noise-based user authentication (DEANUA), which is a verification scheme for enhancing reliability by reducing the error rate and improving robustness. We investigate the current mainstream features and build a more comprehensive feature set that composed of 146 features. Then, we use a differential evolution method to select an optimized feature set. With the support vector regression method on this feature set, we achieve an equal error rate (EER) of 0.12660% and also a 31.25% energy consumption reduction rate. In this paper, the model is trained with the training samples collected from one situation, but the model is used in various situations. Thus, the robustness of the model is inadequate. We constructed the adversarial noise samples to simulate users' behavioral characteristics in different situational contexts. We use the adversarial noise samples to test the models in a strict experimental environment, which raises the EER by 83.59%, to 10.9299%. Then, we enhance the model with adversarial noise samples to obtain an EER of 8.70932%, which is a reduction of 20.32%.

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Section: Introductionmentioning

confidence: 99%

Improving Reliability: User Authentication on Smartphones Using Keystroke Biometrics

Wang

Zheng

et al. 2019

IEEE Access

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“…Contoh penerapan pengenalan wajah dalam menguatkan sistem keamanan smart home dengan menggunakan data latih dari sosial media untuk meningkatkan ketepatan klasifikasinya [3], mengaplikasikan pengenalan wajah dalam identifikasi tindak kejahatan [6,9], dan aplikasi pengenalan wajah untuk pemantuan kehadiran siswa di kelas [16]. Aplikasi-aplikasi tersebut sangat mungkin 32 TEKNIKA, Volume 9(1), Juli 2020, pp. 31 dikembangkan karena adanya kajian tentang ketepatan dalam pengenalan wajah.…”

Section: Pendahuluanunclassified

Penguatan Ketepatan Pengenalan Wajah Viola-Jones Dengan Pelacakan

Florestiyanto

Pratomo

Sari

2020

teknika

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Aplikasi pengenalan wajah sebagian besar berorientasi pada penguatan sistem keamanan dan pemantauan. Aplikasi-aplikasi tersebut banyak dikembangkan akibat adanya kajian penguatan ketepatan pengenalan wajah yang dikembangkan terus-menerus oleh peneliti. Variasi fitur wajah setiap orang yang kompleks dan perubahannya dari waktu ke waktu, bahkan dalam waktu yang singkat menjadikan optimalisasi ketepatan pengenalannya semakin rumit. Studi ini bertujuan untuk meningkatkan performa metode Viola-Jones pada target yang bergerak dengan integrasi algoritma tracking. Algoritma tracking yang diintegrasikan adalah algoritma Continuously Adaptive Mean Shift (Camshift). Algoritma ini merupakan pengembangan dari algoritma Mean Shift yang secara terus menerus melakukan adaptasi atau penyesuaian terhadap distribusi probabilitas warna yang selalu berubah tiap pergantian frame dari sebuah sequence video. Integrasi tracking dengan Viola-Jones signifikan meningkatkan ketepatan pengenalan wajah dibandingkan tanpa tracking yaitu sebesar 96%.

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sAuth: a hierarchical implicit authentication mechanism for service robots

Zhang

Zeng

et al. 2022

J Supercomput

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With advances in networks, Artificial Intelligence (AI), and the Internet of Things, humanoid robots are rising in many areas, including elderly care, companion, education, and services in public sectors. Given their sensing and communication functionality, information leakage and unauthorized access will be of big concern. Very often, authentication techniques for service robots, especially those related to behavioral identification, have been developed, in which behavior models are created using raw data from sensors. However, behavioral-based authentication and re-authentication is still an open area for research, including cold start problems, accuracy, and uncertainty. This paper proposes a hierarchical implicit authentication system by joint built-in sensors and trust evaluation, coined sAuth, which exploits sensor data-based sliding window trust model to identify the service robot and its expected users. In order to mitigate the fluctuations of identification results in the real world environment, the trust evaluation is computed via combining the weighted intermediate identification probability of various small sliding windows. The performance of sAuth is evaluated under different scenarios where we show that (i) approximately 5–7% higher accuracy and 2–18% lower equal error rate can be achieved by our method compared to other works; and (ii) the hierarchical scheme with joint sensors and trust sliding windows improves the authentication accuracy significantly by comparing it with only sensor-based authentication.

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A novel method for real time face spoof recognition for single and multiple user authentication

Cited by 5 publications

References 10 publications

Improving Reliability: User Authentication on Smartphones Using Keystroke Biometrics

Improving Reliability: User Authentication on Smartphones Using Keystroke Biometrics

Penguatan Ketepatan Pengenalan Wajah Viola-Jones Dengan Pelacakan

sAuth: a hierarchical implicit authentication mechanism for service robots

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