Multimodal Biometrics Fusion using Multi-Algorithms

The previous article focused on multi-sensor fusion strategy where a single biometric trait is captured using multiple sensors in order to extract diverse information from the image. A multi-sensor fusion strategy can be implemented as follows using face and fingerprint traits:

Face Biometric System – a system may capture the two dimensional texture content of a person’s face using a 2D camera and a three dimensional surface shape of the face using a 3D camera. The use of multiple sensors, in some instances, can result in the acquisition of complementary information that can enhance the recognition ability of the system. The performance of a 2D face matching system can be improved by utilizing the shape information presented by 3D range images.

Fingerprint Biometric System – a system may capture an individual’s fingerprint images using an optical fingerprint sensor which involves capturing a digital image of the print using visible light ( a specialized digital camera) and capacitive fingerprint sensor which use principles associated with capacitance in order to form fingerprint images. The two sensors provide complementary information and therefore results in enhanced matching accuracy.

Although a multi-sensor strategy has the essential benefits of enhanced accuracy if implemented corrected, the introduction of additional biometric capturing equipments such as a 3D camera to measure the facial surface variation and optical sensor for fingerprint increases the cost of the multimodal biometric system.

Unlike multi-sensor systems highlighted above and in the previous article, in this article we consider a multi-algorithm fusion strategy. Multi-algorithm system uses only a single biometric capturing device (single sensor) to obtain raw data and then the raw biometric data is processed using multiple algorithms. Multi-algorithm fusion strategy can be applied to an Automated Fingerprint Identification System (AFIS) as follows:

AFIS – the same fingerprint image captured using an optical sensor can independently be processed by texture-based fingerprint algorithm and a minutiae-based fingerprint algorithm in order to extract diverse feature sets that can improve the performance of the system.

Multi-algorithm systems do not require the use of additional biometric capturing devices and therefore overcomes the limitations of the Multi-sensor system such as costs. Multi-algorithm strategy can be cost-effective. Furthermore, the user is not required to interact with multiple capturing devices and therefore multi-sensor fusion strategy can enhance user convenience. However, multi-sensor fusion requires the introduction of new feature extractor and/or matcher modules which may increase the computational requirements of the system.

Other fields – Information fusion has also been used in a diversity of scientific fields such as Robotics for navigation. Generally a robot is fitted with a variety of sound, light, image and sensors that allow it to record its environment. In order to determine a suitable action such as move right or tilt camera at a certain angle, the data acquired using these multiple sensors are processed simultaneously.


Our researchers within Biometric Research Laboratory, BRL, at Namibia Biometric Systems will continue to examine the levels of fusion that are possible in a multimodal biometric system in the next article.

More information on the implementation of biometrics based solutions can be requested from