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Conventional biometric systems are vulnerable to a range of harmful threats and privacy violations, putting the users who have registered with them in grave danger. Therefore, there is a need to develop a Privacy-Preserving and Authenticating Framework for Biometric-based Systems (PPAF-BS) that allows users to access multiple applications while also protecting their privacy. There are various existing works on biometric-based systems, but most of them do not address privacy concerns. Conventional biometric systems require the storage of biometric data, which can be easily accessed by attackers, leading to privacy violations. Some research works have used differential privacy techniques to address this issue, but they have not been widely applied in biometric-based systems. The existing biometric-based systems have a significant privacy concern, and there is a lack of privacy-preserving techniques in such systems. Therefore, there is a need to develop a PPAF-BS that can protect the user’s privacy and maintain the system’s efficiency. The proposed method uses Hybrid Deep Learning (HDL) with palmprint, ear, and face biometric features for person identification. Additionally, Discrete Cosine Transform (DCT) feature transformation and Lagrange’s interpolation-based image transformation are used as part of the authentication scheme. Sensors are used to record three biometric traits: palmprint, ear, and face. The combination of biometric characteristics provides an accuracy of 96.4% for the $8\times 8$ image size. The proposed LI-based image transformation lowers the original $512\times 512$ pixels to an $8\times 8$ hidden pattern. This drastically decreases the database size, thereby reducing storage needs. The proposed method offers a safe authentication system with excellent accuracy, a fixed-size database, and the privacy protection of multi-modal biometric characteristics without sacrificing overall system efficiency. The system achieves an accuracy of 96.4% for the $8\times 8$ image size, and the proposed LI-based picture transformation significantly reduces the database size, which is a significant achievement in terms of storage requirements. Therefore, the proposed method can be considered an effective solution to the privacy and security concerns of biometric-based systems.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

Palmprint identification refers to searching in a database for the palmprint template, which is from the same palm as a given palmprint input. The identification process involves preprocessing, feature extraction, feature matching and decision-making. As a key step in the process, in this paper, we propose a new feature extraction method by converting a palmprint image from a spatial domain to a frequency domain using Fourier Transform. The features extracted in the frequency domain are used as indexes to the palmprint templates in the database and the searching process for the best match is conducted by a layered fashion. The experimental results show that palmprint identification based on feature extraction in the frequency domain is effective in terms of accuracy and efficiency.

This paper proposes a score-based indexing technique for biometric databases. A fixed-length index is computed for each image in the database by calculating its match scores against a preselected set of representative images. Further, an efficient storage mechanism (i.e. index space) is developed to arrange the biometric images like traditional database records so that a rapid search is possible. During identification, the retrieval technique finds a list of similar candidates for the query image from the database using voting scheme. Finally, to identify the genuine match from the retrieved similar candidates, we perform a one-one match between the query and each similar candidate using modified Hausdorff distance measure. Experimental results on different databases show a significant performance improvement in terms of response time and identification accuracy compared to the existing indexing methods.

This paper investigates the performance of a bimodal biometric system using fusion of shape and texture. We propose several new hand shape features that can be used to represent the hand shape and improve the performance for hand shape based user authentication. We also demonstrate the usefulness of Discrete Cosine Transform (DCT) coefficients for palmprint authentication. The score level fusion of hand shape and palmprint features using product rule achieves best performance as compared to Max or Sum rule. However the decisions from the Sum, Max, and Product rules can also be combined to further enhance the performance. Thus the fusion of score level decisions, from the multiple strategies, is proposed and investigated. The two hand shapes of an individual are anatomically similar. However, the palmprints from two hands can be combined to further improve performance and is demonstrated in this paper.

This paper presents a new method of personal authentication using face and palmprint images. The facial and palmprint images can be simultaneously acquired by using a pair of digital camera and integrated to achieve higher confidence in personal authentication. The proposed method of fusion uses a feed-forward neural network to integrate individual matching scores and generate a combined decision score. The significance of the proposed method is more than improving performance for bimodal system. Our method uses the claimed identity of users as a feature for fusion. Thus the required weights and bias on individual biometric matching scores are automatically computed to achieve the best possible performance. The experimental results also demonstrate that Sum, Max, and Product rule can be used to achieve significant performance improvement when consolidated matching scores are employed instead of direct matching scores. The fusion strategy used in this paper outperforms even its existing facial and palmprint modules. The performance indices for personal authentication system using two-class separation criterion functions have been analyzed and evaluated. The method proposed in this paper can be extended for any multimodal authentication system to achieve higher performance.

The concept of capturing a palmprint using a scanner is not new, but the current palmprint capture devices based on a scanner have the limitations of low speed and large weight. A new palmprint capture device based on a scanner has been developed, which can overcome these limitations. A scanner based palmprint verification system (PVS) has also been designed using the proposed palmprint capture device. A commercially available mobile internet device (MID) was used to execute the whole program and to store the collected palmprint images. A small palmprint database was built using the developed palmprint capture device and the experimental results on this palmprint database showed that the designed system achieved an acceptable level of performance. The proposed PVS has a stable performance, small size and low price, and can meet the practical needs of civil palmprint applications.

This paper presents a fast palmprint verification system based on fractal coding. In the stage of registration, a sub-image from user's training palmprints is intentionally extracted and stored as his or her template. In the stage of verification, the step of region of interest extraction is not needed, the sample image is directly matched with the template based on fractal coding, which can reduce the whole response time. Whether the sample image and the template are from the same person or not is decided by their matching scores. Experimental evaluation results on two databases clearly demonstrate the effectiveness of the proposed approach.