Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Abstract

Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively.

Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph

“…Therefore, the feature encoding for handprints needs to be designed to incorporate the automated image alignment (for geometrical transformations) and less computational cost. Further, the feature representation of contactless palm print images cannot be directly used in contact images due to low image quality and low resolution because contactless palm print or handprint approaches use multiview image [42,43] and multiresolution [44] image capture with multimodal approaches [45,46]. These strategies help performance-related issues arising from image quality and low resolution.…”

A multimodal biometric database and case study for face recognition based deep learning

“…This might improve overall matching accuracy and strengthen the security of biometric systems. There are several ways to research biometric fusion, one of which makes use of heterogeneous datasets [7], [8], that integrating biometric characteristics (such a fingerprint from a separate database and a signature from another. In the experiment, biometric characteristics from many people are combined to produce a "chimeric user."…”