Kai Cao

An alignment-free fingerprint cryptosystem based on fuzzy vault scheme

Fuzzy vault is a practical and promising scheme, which can protect biometric templates and perform secure key management simultaneously. Alignment of the template biometric sample and the query one in the encrypted domain remains a challenging task. While implementing fuzzy fingerprint vault, the accuracy of alignment in encrypted domain cannot be ensured and the information leakage may be caused because of the alignment. In this paper, an alignment-free fingerprint cryptosystem based on fuzzy vault scheme is developed fusing the local features, known minutia descriptor and minutia local structure, which are invariant to the transformation in fingerprint capturing. Three fusion strategies are employed to integrate the two local features. Huffman coding technology is used to compress the storage volume of the minutia descriptor vault. The proposed fingerprint cryptosystem can avoid the alignment procedure and improve the performance and security of the fuzzy vault scheme at the same time. Experiments on FVC2002-DB2a and FVC2002-DB1a are conducted to show the promising performance of the proposed fingerprint cryptosystem. Security level of proposed cryptosystem will not decrease and even rise in some circumstances. The best trade-off results obtained is GAR=92%(FAR=0), under the 53-bit security. Despite the larger template storage expense, the proposed alignment-free fingerprint cryptosystem outperforms the minutiae-based fingerprint cryptosystems with alignment in the terms of accuracy and security.

Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine Ridge Structure Dictionary.

Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate (“lights-out” capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving “lights-out” latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher.

An effective biometric cryptosystem combining fingerprints with error correction codes

With the emergence and popularity of identity verification means by biometrics, the biometric system which can assure security and privacy has received more and more concentration from both the research and industry communities. In the field of secure biometric authentication, one branch is to combine the biometrics and cryptography. Among all the solutions in this branch, fuzzy commitment scheme is a pioneer and effective security primitive. In this paper, we propose a novel binary length-fixed feature generation method of fingerprint. The alignment procedure, which is thought as a difficult task in the encrypted domain, is avoided in the proposed method due to the employment of minutiae triplets. Using the generated binary feature as input and based on fuzzy commitment scheme, we construct the biometric cryptosystems by combining various of error correction codes, including BCH code, a concatenated code of BCH code and Reed-Solomon code, and LDPC code. Experiments conducted on three fingerprint databases, including one in-house and two public domain, demonstrate that the proposed binary feature generation method is effective and promising, and the biometric cryptosystem constructed by the feature outperforms most of the existing biometric cryptosystems in terms of ZeroFAR and security strength. For instance, in the whole FVC2002 DB2, a 4.58% ZeroFAR is achieved by the proposed biometric cryptosystem with the security strength 48 bits.

Multi-scale local binary pattern with filters for spoof fingerprint detection

Fingerprint recognition systems are being increasingly deployed in both government and civilian applications. But the emergence of fake fingerprints brings on a new challenge. Among the numerous fingerprint vitality detection methods, local binary pattern (LBP) is considered as one of the best operators. But the original LBP operator has the limitation of its small spatial support area. So we proposed a novel spoof fingerprint detection method based on multi-scale local binary pattern (MSLBP). Generally speaking, the MSLBP can be realized in two different ways. We implemented both types of MSLBP. Each MSLBP was combined with a set of filters. In this way, each sample in the LBP circle could be made to collect intensity information from a large area rather than a single pixel. The experimental results in the database of the Liveness Detection Competition 2011 (LivDet2011) have shown that both types of MSLBP are effective and superior in spoof fingerprint detection.

Fingerprint classification by a hierarchical classifier

Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.

A novel ant colony optimization algorithm for large-distorted fingerprint matching

Large distortion may be introduced by non-orthogonal finger pressure and 3D-2D mapping during the process of fingerprint capturing. Furthermore, large variations in resolution and geometric distortion may exist among the fingerprint images acquired from different types of sensors. This distortion greatly challenges the traditional minutiae-based fingerprint matching algorithms. In this paper, we propose a novel ant colony optimization algorithm to establish minutiae correspondences in large-distorted fingerprints. First, minutiae similarity is measured by local features, and an assignment graph is constructed by local search. Then, the minutiae correspondences are established by a pseudo-greedy rule and local propagation, and the pheromone matrix is updated by the local and global update rules. Finally, the minutiae correspondences that maximize the matching score are selected as the matching result. To compensate resolution difference of fingerprint images captured from disparate sensors, a common resolution method is adopted. The proposed method is tested on FVC2004 DB1 and a FINGERPASS cross-matching database established by our lab. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of large-distorted fingerprint matching, especially for those fingerprint images acquired from different modes of acquisition.

Combining features for distorted fingerprint matching

Extracting and fusing discriminative features in fingerprint matching, especially in distorted fingerprint matching, is a challenging task. In this paper, we introduce two novel features to deal with nonlinear distortion in fingerprints. One is finger placement direction which is extracted from fingerprint foreground and the other is ridge compatibility which is determined by the singular values of the affine matrix estimated by some matched minutiae and their associated ridges. Both of them are fixed-length and easy to be incorporated into matching score. In order to improve the matching performance, we combine these two features with orientation descriptor and local minutiae structure, which are used to measure minutiae similarity, to achieve fingerprint matching. In addition, we represent minutiae set as a graph and use graph connect component and iterative robust least square (IRLS) to detect creases and remove spurious minutiae close to creases. Experimental results on FVC2004 DB1 and DB3 demonstrate that the proposed algorithm could obtain promising results. The equal error rates (EER) are 3.35% and 1.49% on DB1 and DB3, respectively.

Learning Fingerprint Reconstruction: From Minutiae to Image

The set of minutia points is considered to be the most distinctive feature for fingerprint representation and is widely used in fingerprint matching. It was believed that the minutiae set does not contain sufficient information to reconstruct the original fingerprint image from which minutiae were extracted. However, recent studies have shown that it is indeed possible to reconstruct fingerprint images from their minutiae representations. Reconstruction techniques demonstrate the need for securing fingerprint templates, improving the template interoperability, and improving fingerprint synthesis. But, there is still a large gap between the matching performance obtained from original fingerprint images and their corresponding reconstructed fingerprint images. In this paper, the prior knowledge about fingerprint ridge structures is encoded in terms of orientation patch and continuous phase patch dictionaries to improve the fingerprint reconstruction. The orientation patch dictionary is used to reconstruct the orientation field from minutiae, while the continuous phase patch dictionary is used to reconstruct the ridge pattern. Experimental results on three public domain databases (FVC2002 DB1_A, FVC2002 DB2_A, and NIST SD4) demonstrate that the proposed reconstruction algorithm outperforms the state-of-the-art reconstruction algorithms in terms of both: 1) spurious minutiae and 2) matching performance with respect to type-I attack (matching the reconstructed fingerprint against the same impression from which minutiae set was extracted) and type-II attack (matching the reconstructed fingerprint against a different impression of the same finger).

LFIQ: Latent fingerprint image quality

Latent fingerprint images are typically obtained under non-ideal acquisition conditions, resulting in incomplete or distorted impression of a finger, and ridge structure corrupted by background noise. This necessitates involving latent experts in latent fingerprint examination, including assessing the value of a latent print as forensic evidence. However, it is now generally agreed that human factors (e.g., human visual perception, expertise of latent examiners, workload, etc.) can significantly affect the reliability and consistency of the value determinations made by latent examiners. We propose an objective quality measure for latent fingerprints, called Latent Fingerprint Image Quality (LFIQ), that can be effectively used to distinguish latent fingerprints of good quality, which do not require any human intervention, and to compensate for the subjective nature of value determination by latent examiners. We investigate several factors that determine the latent quality: (i) ridge quality based on ridge clarity and connectivity of good ridge structures, (ii) minutiae reliability based on a minutiae dictionary learnt from high quality minutia patches, and (iii) position of the finger by detecting a reference point. The proposed LFIQ metric is based on triangulation of minutiae incorporating the above three factors. Experimental results show that (i) the proposed LFIQ is a good predictor of the latent matching performance by AFIS and (ii) it is also correlated with value determination by latent examiners.

Recognizing infants and toddlers using fingerprints: Increasing the vaccination coverage

One of the major goals of most national, international and non-governmental health organizations is to eradicate the occurrence of vaccine-preventable childhood diseases (e.g., polio). Without a high vaccination coverage in a country or a geographical region, these deadly diseases take a heavy toll on children. Therefore, it is important for an effective immunization program to keep track of children who have been immunized and those who have received the required booster shots during the first 4 years of life to improve the vaccination coverage. Given that children, as well as the adults, in low income countries typically do not have any form of identification documents which can be used for this purpose, we address the following question: can fingerprints be effectively used to recognize children from birth to 4 years? We have collected 1,600 fingerprint images (500 ppi) of 20 infants and toddlers captured over a 30-day period in East Lansing, Michigan and 420 fingerprints of 70 infants and toddlers at two different health clinics in Benin, West Africa. We devised the following strategies to improve the fingerprint recognition accuracy when comparing the acquired fingerprints against an extended gallery database of 32,768 infant fingerprints collected by VaxTrac in Benin: (i) upsample the acquired fingerprint image to facilitate minutiae extraction, (ii) match the query print against templates created from each enrollment impression and fuse the match scores, (iii) fuse the match scores of the thumb and index finger, and (iv) update the gallery with fingerprints acquired over multiple sessions. A rank-1 (rank-10) identification accuracy of 83.8% (89.6%) on the East Lansing data, and 40.00% (48.57%) on the Benin data is obtained after incorporating these strategies when matching infant and toddler fingerprints using a commercial fingerprint SDK. This is an improvement of about 38% and 20%, respectively, on the two datasets without using the proposed strategies. A state-of-the-art latent finger-print SDK achieves an even higher rank-1 (rank-10) identification accuracy of 98.97% (99.39%) and 67.14% (71.43%) on the two datasets, respectively, using these strategies; an improvement of about 23% and 24%, respectively, on the two datasets without using the proposed strategies.