Search Results (7)

Contact-based biometric applications primarily use prints from a finger or a palm for a single instance in different applications. For access control, there is an enrollment process using one or more templates which are compared with verification images. In forensics applications, randomly located, partial, and often degraded prints acquired from a crime scene are compared with the images captured from suspects or existing fingerprint databases, like AFIS. In both scenarios, if we need to use handprints which include segments from the finger and palm, what would be the solution? The motivation behind this is the concept of one single algorithm for one hand. Using an algorithm that can incorporate both prints in a common processing framework can be an alternative which will have advantages like scaling to larger existing databases. This work proposes a method that uses minutiae or minutiae-like features, Delaunay triangulation and graph matching with invariant feature representation to overcome the effects of rotation and scaling. Since palm prints have a large surface area with degradation, they tend to have many false minutiae compared to fingerprints, and the existing palm print algorithms fail to tackle this. The proposed algorithm constructs Delaunay triangulated graphs (DTG) using minutiae where Delaunay triangles form from minutiae, and initiate a collection of base triangles for opening the matching process. Several matches may be observed for a single triangle match when two images are compared. Therefore, the set of initially matched triangles may not be a true set of matched triangles. Each matched triangle is then used to extend as a sub-graph, adding more nodes to it until a maximum graph size is reached. When a significant region of the template image is matched with the test image, the highest possible order of this graph will be obtained. To prove the robustness of the algorithm to geometrical variations and working ability with extremely degraded (similar to latent prints) conditions, it is demonstrated with a subset of partial-quality and extremely-low-quality images from the FVC (fingerprint) and the THUPALMLAB (palm print) databases with and without geometrical variations. The algorithm is useful when partial matches between template and test are expected, and alignment or geometrical normalization is not accurately possible in pre-processing. It will also work for cross-comparisons between images that are not known a priori. Full article

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching. Full article

Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%. Full article

Biometric authentication systems raise certain concerns with regard to security, violation of privacy, and storage issues of biometric templates. This paper proposes a protection approach of biometric templates storage in a multimodal biometric system while ensuring both the cancelability of biometric templates and the efficiency of the authentication process. We propose applying a chaotic maps-based transform on the biometric features to address the cancelability issue. We used Logistic map and Torus Automorphism to generate cancelable biometric features of the face and fingerprint minutia points, respectively. Both transformed features would be concatenated and saved in the database of the system instead of the original features. In the authentication stage, the similarity scores of both transformed face and fingerprint templates are computed and fused using the weighted sum rule. The results of the experimentation, conducted using images from the ORL face and FVC2002 DB1 fingerprint databases, demonstrated the higher performance of the proposed approach achieving a genuine accept rate equal to $100\%$. Moreover, the obtained results confirmed the soundness of the proposed cancelable technique to satisfy the biometric systems’ requirements (i.e., security, revocability, and diversity). Full article

Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique. Full article

Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets. Full article

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples. Full article