Byung-Jun Son

Embedded Panoramic Mosaic System Using Auto-Shot Interface

This paper proposes a panoramic image mosaic system for mobile devices. The proposed panoramic mosaic system is optimized for mobile devices by integer- programmable algorithms, and is user-friendly designed by 4- directional auto-shot interface. The proposed system consists of image acquisition by auto-shot interface, image transform onto cylindrical mosaic surface, luminance and color compensation, local alignment, image stitching, and blending. The images are automatically captured by the auto-shot interface which senses the users camera motion and takes a picture when the camera motion is matched to the predefined motion model of images. The captured images are transformed and projected onto a cylindrical mosaic surface. Exposure difference is removed by color compensation, and the projected images are locally aligned by translational motion model. Then, the boundary of overlapped images are determined and synthesized by image stitching and blending. According to the experiments using real mobile devices, the proposed mosaic system shows good performance compared with other mosaic algorithms. Especially, since the proposed mosaic system operates on the usual mobile multimedia in real-time, the users can generate panoramic images easier than the personal computer based algorithms.

An empirical study on iris recognition in a mobile phone

Address issues from the implementing iris recognition system on mobile phone.Propose optimal wavelength and installation position of illuminators for the system.An optimal users gazing point is proposed to minimize occlusion and off-angle.Eye detection algorithm can detect an eye in real time on mobile phone.BERC mobile-iris database is constructed autonomously by using a proposed system. 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 users 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 850nm LEDs were selected for iris illumination at 1.1cm away from the iris camera for the size of a 7cm ? 13.7cm phone. In the performance, the recognition accuracy was 0.1% EER (equal error rate) and the eye detection rate with the speed of 17.64ms on a mobile phone was 99.4%.

An empirical study of palmprint recognition for mobile phones

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%.

An eye detection method robust to eyeglasses for mobile iris recognition

An eye detection method that is robust to eyeglass interference is proposed for mobile iris recognition system.Multi-scale window mask consisting of 2ź×ź3 subblocks is used to generate eye candidates.Eye validation is applied to select the true eye image among the eye candidates.The proposed method performs more accurately and quickly than competing methods in mobile environment. 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 2ź×ź3 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 3ź×ź3 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.

Empirical study on touchless fingerprint recognition using a phone camera

Abstract. 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.