  Only a few studies have been reported regarding human ear recognition in long
wave infrared band. Thus, we have created ear database based on long wave
infrared band. We have called that the database is long wave infrared band
MIDAS consisting of 2430 records of 81 subjects. Thermal band provides seamless
operation both night and day, robust against spoofing with understanding live
ear and invariant to illumination conditions for human ear recognition. We have
proposed to use different algorithms to reveal the distinctive features. Then,
we have reduced the number of dimensions using subspace methods. Finally, the
dimension of data is reduced in accordance with the classifier methods. After
this, the decision is determined by the best sores or combining some of the
best scores with matching fusion. The results have showed that the fusion
technique was successful. We have reached 97.71% for rank-1 with 567 test
probes. Furthermore, we have defined the perfect rank which is rank number when
recognition rate reaches 100% in cumulative matching curve. This evaluation is
important for especially forensics, for example corpse identification, criminal
investigation etc.

Computer Vision and Pattern Recognition