Latent fingerprint recognition is not a new topic but it has attracted a lot of attention from researchers in both academia and industry over the past 50 years. With the rapid development of pattern recognition techniques, automated fingerprint identification systems (AFIS) have become more and more ubiquitous. However, most AFIS are utilized for live-scan or rolled slap prints while only a few systems can work on latent fingerprints with reasonable accuracy. The question of whether taking higher resolution scans of latent fingerprints and their rolled slap mate prints could help improve the identification accuracy still remains an open question in the forensic community. Because pores are one of the most reliable features besides minutiae to identify latent fingerprints, we propose an end-to-end automatic pore extraction and matching system to analyze the utility of pores in latent fingerprint identification. Hence, this paper answers two questions in the latent fingerprint domain: (i) does the incorporation of pores as level-3 features improve the system performance significantly? and (ii) does the 1,000 ppi image resolution improve the recognition results? We believe that our proposed end-to-end pore extraction and matching system will be a concrete baseline for future latent AFIS development.
There have been several attempts made to use pores as a feature for matching. Jain al @cite_30 designed a hand-crafted approach to utilize features from all 3 fingerprint feature levels. They used pre-defined thresholds for their matching system. Again, they only reported experimental results on their high-resolution lab-collected dataset. Chen and Jain @cite_9 extracted an extended feature set as input for the matching step. They used a commercial off-the-shelf (COTS) matcher to evaluate their feature extraction. The dataset on which they evaluated is the partial fingerprint dataset from NIST SD30 This dataset is no longer publicly available. . Zhao al @cite_31 proposed a pore matcher based on a combination of the ICP @cite_24 and RANSAC @cite_16 algorithms. Again, they only evaluated their algorithm on their high-resolution live-scanned partial fingerprint images.
@cite_30 @cite_9 @cite_24 @cite_31 @cite_16
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Sweat pores on fingerprints have proven to be useful features for personal identification. Several methods have been proposed for pore matching. The state-of-the-art method first matches minutiae on the fingerprints and then matches the pores based on the minutia matching results. A problem of such minutia-based pore matching method is that the pore matching is dependent on the minutia matching. Such dependency limits the pore matching performance and impairs the effectiveness of the fusion of minutia and pore match scores. In this paper, we propose a novel direct approach for matching fingerprint pores. It first determines the correspondences between pores based on their local features. It then uses the RANSAC (RANdom SAmple Consensus) algorithm to refine the pore correspondences obtained in the first step. A similarity score is finally calculated based on the pore matching results. The proposed pore matching method successfully avoids the dependency of pore matching on minutia matching results. Experiments have shown that the fingerprint recognition accuracy can be greatly improved by using the method proposed in this paper. Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, level 1 (pattern), level 2 (minutia points), and level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of level 3 features to assist in identification, automated fingerprint identification systems (AFIS) currently rely only on level 1 and level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi 1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the iterative closest point (ICP) algorithm. Our experiments show that level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when level 3 features are employed in combination with level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images Abstract Most fingerprint recognition systems use Level 1 characteristics (ridge flow, orientation, and frequency) and Level 2 features (minutiae points) to recognize individuals. Level 3 features (sweat pores, incipient ridges and ultra-thin characteristics of the ridges) are less frequently adopted because they can be extracted only from high resolution images, but they have the potential of improving all the steps of the biometric recognition process. In particular, sweat pores can be used for quality assessment, liveness detection, biometric matching in live applications, and matching of partial latent fingerprints in forensic applications. Currently, each type of fingerprint acquisition technique (touch-based, touchless, or latent) requires a different algorithm for pore extraction. In this paper, we propose the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images. Our method uses specifically designed and trained Convolutional Neural Networks (CNN) to estimate and refine the centroid of each pore. Results show that our method is feasible and achieved satisfactory accuracy for all the types of evaluated images, with a better performance with respect to the compared state-of-the-art methods. Sweat pores on fingerprints have proven to be discriminative features and have recently been successfully employed in automatic fingerprint recognition systems (AFRS), where the extraction of fingerprint pores is a critical step. Most of the existing pore extraction methods detect pores by using a static isotropic pore model; however, their detection accuracy is not satisfactory due to the limited approximation capability of static isotropic models to various types of pores. This paper presents a dynamic anisotropic pore model to describe pores more accurately by using orientation and scale parameters. An adaptive pore extraction method is then developed based on the proposed dynamic anisotropic pore model. The fingerprint image is first partitioned into well-defined, ill-posed, and background blocks. According to the dominant ridge orientation and frequency on each foreground block, a local instantiation of appropriate pore model is obtained. Finally, the pores are extracted by filtering the block with the adaptively generated pore model. Extensive experiments are performed on the high resolution fingerprint databases we established. The results demonstrate that the proposed method can detect pores more accurately and robustly, and consequently improve the fingerprint recognition accuracy of pore-based AFRS.