  The development of technology biometrics becomes crucial more. To define
human characteristic biometric systems are used but because of inability of
traditional biometric systems to recognize twins, multimodal biometric systems
are developed. In this study a multimodal biometric recognition system is
proposed to recognize twins from each other and from the other people by using
image and speech data. The speech or image data can be enough to recognize
people from each other but twins cannot be distinguished with one of these
data. Therefore a robust recognition system with the combine of speech and ear
images is needed. As database, the photos and speech data of 39 twins are used.
For speech recognition MFCC and DTW algorithms are used. Also, Gabor filter and
DCVA algorithms are used for ear identification. Multi-biometrics success rate
is increased by making matching score level fusion. Especially, rank-5 is
reached 100%. We think that speech and ear can be complementary. Therefore, it
is result that multi-biometrics based speech and ear is effective for human
identifications.
