Face Recognition Based on Near-Infrared Light Using Mobile Phone

Han, Song-Yi; Park, Hyun-Ae; Cho, Dal-Ho; Park, Kang Ryoung; Lee, Sangyoun

doi:10.1007/978-3-540-71629-7_50

Cited by 12 publications

(10 citation statements)

References 11 publications

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“…An overview of the proposed method is shown in Figure 1 [16]. First, the user initiates the iris recognition process by clicking the "start" button of a mobile phone.…”

Section: Overview Of the Proposed Methods And The Illuminator On/off Smentioning

confidence: 99%

“…We used the focus checking method proposed by Kang and Park [19]. Figure 2: The alternative on/off scheme of the dual IR-illuminators [16].…”

Section: Overview Of the Proposed Methods And The Illuminator On/off Smentioning

confidence: 99%

“…Also, only the iris detection accuracy and processing times were shown. In [16], the researchers also used the on/off illuminator scheme, but it was used for detecting rough eye positions for face recognition.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Iris Localization and Recognition on Mobile Phones

Park

Kang

et al. 2007

EURASIP J. Adv. Signal Process.

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A new iris recognition method for mobile phones based on corneal specular reflections (SRs) is discussed. We present the following three novelties over previous research. First, in case of user with glasses, many noncorneal SRs may happen on the surface of glasses and it is very difficult to detect genuine SR on the cornea. To overcome such problems, we propose a successive on/off dual illuminator scheme to detect genuine SRs on the corneas of users with glasses. Second, to detect SRs robustly, we estimated the size, shape, and brightness of the SRs based on eye, camera, and illuminator models. Third, the detected eye (iris) region was verified again using the AdaBoost eye detector. Experimental results with 400 face images captured from 100 persons with a mobile phone camera showed that the rate of correct iris detection was 99.5% (for images without glasses) and 98.9% (for images with glasses or contact lenses). The consequent accuracy of iris authentication was 0.05% of the EER (equal error rate) based on detected iris images.

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“…An overview of the proposed method is shown in Figure 1 [16]. First, the user initiates the iris recognition process by clicking the "start" button of a mobile phone.…”

Section: Overview Of the Proposed Methods And The Illuminator On/off Smentioning

confidence: 99%

“…We used the focus checking method proposed by Kang and Park [19]. Figure 2: The alternative on/off scheme of the dual IR-illuminators [16].…”

Section: Overview Of the Proposed Methods And The Illuminator On/off Smentioning

confidence: 99%

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A Study on Iris Localization and Recognition on Mobile Phones

Park

Kang

et al. 2007

EURASIP J. Adv. Signal Process.

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“…Mobile devices only play a role as image acquisition component. Han's method seems to be faster than HCFA. However, it is because of the high efficiency of the programming language (C/C++) and high computing power of the testbed rather than the algorithm itself.…”

Section: Java Platform Micro Edition Implementationmentioning

confidence: 99%

Mobile device access control: an improved correlation based face authentication scheme and its Java ME application

Han

2011

Concurrency and Computation

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SUMMARY This paper investigates face authentication based access control solutions for camera‐equipped mobile devices. A new hierarchical correlation based face authentication (HCFA) scheme is proposed, which suits resource‐constrained mobile devices such as mobile phones and personal digital assistants. The idea of HCFA is conducting a partial correlation output peak analysis (analyze the relationship between each cross‐correlation output peak generated from selected sub‐regions of a face), in conjunction with conventional direct cross‐correlation methods. The experimental results on the public domain database demonstrate that the proposed scheme achieved better performance than that of the conventional direct correlation based schemes. Furthermore, HCFA was implemented on the Nokia S60 CLDC emulator using Java ME (previously J2ME) programming technology in order to test the applicability and implementability. The test results show that the proposed algorithm is implementable on mobile devices. It not only shortens processing time but also reduces resource demand significantly, compared with the direct correlation algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

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“…Biometrics could reasonably represent a more practical and reliable way to perform person authentication [25] by exploiting the sensors typically embedded in this class of devices (multiple cameras, microphone, accelerometers, gyros, digital ✩ This paper has been recommended for acceptance by G. Sanniti compass, fingerprint sweep-scanner, etc.). Face is perhaps the first biometric proposed for person-authentication on mobile devices Song-yi et al [13], eventually combined with voice [24], but the growing imaging quality featured by the last generations of smartphones and tablets provides the technological premise to perform iris recognition as well. Iris, indeed, is inherently characterized by high discriminating power and stability over time [17] and it therefore represents a good candidate for the aforementioned applications, though its small size (compared to the whole face) makes capturing the intricate texture details responsible for its saliency a challenging task.…”

Section: Introductionmentioning

confidence: 99%