We present an optical imaging system based on compressive sensing (CS) along with its principal mathematical aspects. Although CS is undergoing significant advances and empowering many discussions and applications throughout various fields, this article focuses on the analysis of a single-pixel camera. This work was the core for the development of a single-pixel camera approach based on active illumination. Therefore, the active illumination concept is described along with the experimental results, which were very encouraging toward the development of compressive-sensing-based cameras for various applications, such as pixel-level programmable gain imaging.

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Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincaré Indexbased methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5% to 7%.

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