  Biometric recognition systems have advanced significantly in the last decade
and their use in specific applications will increase in the near future. The
ability to conduct meaningful comparisons and assessments will be crucial to
successful deployment and increasing biometric adoption. The best modality used
as unimodal biometric systems are unable to fully address the problem of higher
recognition rate. Multimodal biometric systems are able to mitigate some of the
limitations encountered in unimodal biometric systems, such as
non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof
attacks, and performance. More reliable recognition accuracy and performance
are achievable as different modalities were being combined together and
different algorithms or techniques were being used. The work presented in this
paper focuses on a bimodal biometric system using face and fingerprint. An
image enhancement technique (histogram equalization) is used to enhance the
face and fingerprint images. Salient features of the face and fingerprint were
extracted using the Gabor filter technique. A dimensionality reduction
technique was carried out on both images extracted features using a principal
component analysis technique. A feature level fusion algorithm (Mahalanobis
distance technique) is used to combine each unimodal feature together. The
performance of the proposed approach is validated and is effective.
