Multimodal biometric systems allow to overcome some of the problems presented in unimodal systems, such as non-universality, lack of distinctiveness of the unimodal trait, noise in the acquired data, etc. Integration at the matching score level is the most common approach used due to the ease in combining the scores generated by different unimodal systems. Unfortunately, scores usually lie in application-dependent domains. In this work, we use linear logistic regression fusion, in which fused scores tend to be calibrated log-likelihood-ratios and thus, independent of the application. We use for our experiments the development set of scores of the <i>DS2 Evaluation (Access Control Scenario)</i> of the BioSecure Multimodal Evaluation Campaign, whose objective is to compare the performance of fusion algorithms when query biometric signals are originated from heterogeneous biometric devices. We compare a fusion scheme that uses linear logistic regression with a set of simple fusion rules. It is observed that the proposed fusion scheme outperforms all the simple fusion rules, with the additional advantage of the application-independent nature of the resulting fused scores.
Although many image quality measures have been proposed
for fingerprints, few works have taken into account how differences
among capture devices impact the image quality. Several
representative measures for assessing the quality of fingerprint images
are compared using an optical and a capacitive sensor. We
implement and test a representative set of measures that rely on
different fingerprint image features for quality assessment. The capability
to discriminate between images of different quality and the
relationship with the verification performance are studied. For our
verification experiments, we use minutiae- and ridge-based matchers,
which are the most common approaches for fingerprint recognition.
We report differences depending on the sensor, and interesting
relationships between sensor technology and features used for
quality assessment are also pointed out.
MEHIDA is a multimedia system offering hearing-impaired children an easy and attractive method to communicate with their hearing and deaf peers. It is a TOTAL COMMUNICATION method whose objective is the acquisition of various forms of communication available to the hearing impaired simultaneously: gesture, speech, dactylology, formal signing, lip reading, reading and writing. Didactic activities and games are used to teach the different means of communication. The approach gives the child the chance to practice the different types of communication. A character has been created in the shape of a pear to assist and guide the child. The pupil identifies with the character at all times, as it explains what the child is being asked to do during each activity. The MEHIDA learning process is divided into six stages: basic learning, prereading and prewriting, syllable, word, simple and complex sentence reading and writing. Each phase establishes a hierarchy of didactic objectives which are the expression of the skills and knowledge to be acquired by the child during the learning process (e.g., learning concepts of similarity) broken down into a series of lower level operational objectives (e.g., select figures of the same shape, size and color).