Biometrics Questions
Multimodal biometrics refers to the use of multiple biometric traits or characteristics for identification or authentication purposes. The process involves capturing and analyzing data from two or more biometric modalities, such as fingerprints, iris patterns, facial features, voice, or behavioral traits like gait or typing patterns.
First, the individual's biometric data is collected using various sensors or devices specific to each modality. For example, fingerprints are captured using a fingerprint scanner, while facial features are captured through a camera.
Next, the collected biometric data is pre-processed to enhance its quality and extract relevant features. This step involves removing noise, normalizing the data, and extracting distinctive characteristics that are unique to each individual.
After pre-processing, the extracted features from each modality are combined or fused to create a multimodal biometric template. This template represents a comprehensive and more reliable representation of an individual's identity, as it incorporates multiple biometric traits.
During the identification or authentication process, the individual's biometric data is captured again, and the same pre-processing and feature extraction steps are applied. The extracted features are then compared with the stored multimodal biometric template.
Matching algorithms are used to compare the extracted features with the stored template and determine the level of similarity or dissimilarity. The decision is made based on a predefined threshold, which determines whether the individual is accepted or rejected.
Multimodal biometrics offers several advantages over single-modal biometrics, including increased accuracy, robustness, and resistance to spoofing attacks. By combining multiple biometric traits, the system can compensate for the limitations or weaknesses of individual modalities, resulting in a more reliable and secure identification or authentication process.