We propose a simple and effective approach for fingerprint liveness detection based on the wavelet analysis of the finger tip surface texture. Experimental results show that our method can successfully differentiate live finger tips from fake finger tips made of most commonly used material in fingerprint spoofing.
Abstract. Most high dynamic range image (HDRI) algorithms assume stationary scene for registering multiple images which are taken under different exposure settings. In practice, however, there can be some global or local movements between images caused by either camera or object motions. This situation usually causes ghost artifacts which make the same object appear multiple times in the resultant HDRI. To solve this problem, most conventional algorithms conduct ghost detection procedures followed by ghost region filling with the estimated radiance values. However, usually these methods largely depend on the accuracy of the ghost detection results, and thus often suffer from color artifacts around the ghost regions. In this paper, we propose a new robust ghost-free HDRI generation algorithm that does not require accurate ghost detection and not suffer from the color artifact problem. To deal with the ghost problem, our algorithm utilizes the global intensity transfer functions obtained from joint probability density functions (pdfs) between different exposure images. Then, to estimate reliable radiance values, we employ a generalized weighted filtering technique using the global intensity transfer functions. Experimental results show that our method produces the state-of-the-art performance in generating ghost-free HDR images.
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