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Existing methods for touchless fingerprint recognition using a phone camera have the difficulties to quickly verify and extract fingerprint images with high recognition accuracy. It is because specialized techniques for touchless fingerprint images were not provided for them. To overcome these difficulties, three methods are proposed. First, a finger-shaped guide is displayed on the phone screen to enable the user to position their fingers correctly and to focus the camera on the position to quickly produce a good fingerprint image. Second, the profile line tests at selected points on the guide are proposed to quickly determine whether the user's fingers are properly placed according to the guide shape and to simplify the segmentation of fingerprint image regions. Third, a existing local structure-based matching method is improved for touchless fingerprint images, and a score-level fusion of the three finger matchings is performed to ensure high recognition accuracy. Experiments were performed using our custom-built database, where touchless fingerprint images were collected using a 13-MP mobile camera in various indoor and outdoor environments. Resulting error rates were between 0.50% and 1.04% depending on whether the image was captured indoors or outdoors. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033038] [ABSTRACT FROM AUTHOR]

Finding the accurate position of an eye is crucial for mobile iris recognition system in order to extract the iris region quickly and correctly. Unfortunately, this is very difficult to accomplish when a person is wearing eyeglasses because of the interference from the eyeglasses. This paper proposes an eye detection method that is robust to eyeglass interference in mobile environment. The proposed method comprises two stages: eye candidate generation and eye validation. In the eye candidate generation stage, multi-scale window masks consisting of 2 × 3 subblocks are used to generate all image blocks possibly containing an eye image. In the ensuing eye validation stage, two methods are employed to determine which blocks actually contain true eye images and locate their precise positions as well: the first method searches for the glint of an NIR illuminator on the pupil region. If this first method fails, the next method computes the intensity difference between the assumed pupil and its surrounding region using multi-scale 3 × 3 window masks. Experimental results show that the proposed method detects the eye position more accurately and quickly than competing methods in the presence of interference from eyeglass frames. [ABSTRACT FROM AUTHOR]

Limited processing power, hand pose variations, complicated backgrounds and changing illumination are some of the inherent problems in mobile palmprint recognition. In this paper, a hand-shaped guide window is proposed for fast processing of image acquisition, valley point detection and verification to deal with such problems. Also, an improved version of the Competitive Code is proposed in order to cope with the variation in mobile images, and two practical verification scenarios are suggested to further improve the verification performance. A palmprint database was established using mobile phones for experiments, and an equal error rate (EER) of 2.88% was achieved by using the conventional 1-to-1 matching strategy. By applying the suggested practical scenarios, the verification accuracy was improved to achieve an EER of 0.97%. [ABSTRACT FROM AUTHOR]

The iris recognition on a mobile phone is different from the conventional iris recognition implemented on a dedicated device in that the computational power of a mobile phone and the space for placing NIR (near infrared) LED (light emitting diode) illuminators and iris camera are limited. This paper raises these issues in detail based on real implementation of an iris recognition system in a mobile phone and proposes some solutions to these issues. An experimental study was conducted to search for the relevant power and wavelength of NIR LED illuminators with their positioning on a phone for capturing a good quality iris image. Subsequently, in view of the disparity between the user's gazing point and the center of the iris camera which causes degradation of acquired iris images, an experiment was performed to locate the appropriate gazing point for good iris image capture. A fast eye detection algorithm was proposed for implementation under the mobile platform with low computational facility. The experiments were conducted on a currently released mobile phone and the results showed promising potential for adoption of iris recognition as a reliable authentication means. As a result, two 850 nm LEDs were selected for iris illumination at 1.1 cm away from the iris camera for the size of a 7 cm × 13.7 cm phone. In the performance, the recognition accuracy was 0.1% EER (equal error rate) and the eye detection rate with the speed of 17.64 ms on a mobile phone was 99.4%. [ABSTRACT FROM AUTHOR]