The Identification Of Ear Prints Using Complex Gabor Filters

Biometrics is a method used to recognize humans based on one or a few characteristicsphysical or behavioral traits that are unique such as DNA, face, fingerprints, gait, iris, palm, retina,signature and sound. Although the facts that ear prints are found in 15% of crime scenes, ear printsresearch has been very limited since the success of fingerprints modality. The advantage of the useof ear prints, as forensic evidence, are it relatively unchanged due to increased age and have fewervariations than faces with expression variation and orientation. In this research, complex Gaborfilters is used to extract the ear prints feature based on texture segmentation. Principal componentanalysis (PCA) is then used for dimensionality-reduction where variation in the dataset ispreserved. The classification is done in a lower dimension space defined by principal componentsbased on Euclidean distance. In experiments, it is used left and right ear prints of ten respondentsand in average, the successful recognition rate is 78%. Based on the experiment results, it isconcluded that ear prints is suitable as forensic evidence mainly when combined with otherbiometric modalities.


INTRODUCTION
Surveillance technologies now widely used by the society. The very fast growth of security cameras is caused by the numbers of increasing crimes, and also the environmental needs that are safe but lack of labors to oversee the neighborhood. The non-invasive biometric technology users are the important since they improve the usability of the security cameras by building automated visual surveillance systems. Non-invasive biometric technologies is the example of the improvement by building the facial recognition and the ear prints that are useful for forensic investigation based on the camera records. Therefore, nowadays, criminals are familiar with disguises to hide their identities, so the facial recognition becomes less effective for this forensic investigation needs. Here, the ear prints recognition can be the alternative way that is promising. For example, it can be seen in Fig. 1, the suspect uses the pet cap to hide his identity. However, his ear can be clearly seen, although with a low resolution. Notice that the disguise, that is used to avoid the identification, can be socially accepted. Human ears are the very stable structures, which are rich in information and easily imaged for biometric identifications. The biometric of ear prints is often compared with the biometric of face [1]. Ear has several excellences rather compared face, especially because ear is relatively unchanged due to increased age, colors of skin, and hair styles [2]. It has fewer variations than faces with expressions variations and orientations [3]. The uniqueness of the form of ear to any human being is useful for forensic investigation, because it is a

LTS AND D
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ON
gram, ten images of spondents periments gnitions is .
36 Based on the test result to the reidentification of the image of the left and right ears each as many as ten times experiments, it is obtained the success level of right ear recognition is seventy five percent and the left ear is eighty percent. So, in average the success level of ears recognition is seventy eight percent. This result shows the recognitions of ear prints still cannot be the principal feature for the authentication application, which needs a high accuracy and easy in the detection. But, the ear print is quite adequate, if it is used as an additional feature for the forensic and reconnaissance requirements because it can provide clear hints, besides the ear print is relatively permanent and the image of the ear is also easily obtained.

CONCLUSION
The application program of the ear identification, by using the complex Gabor filters method for the extraction feature, has a high level of success in identifying the ears of the respondents. This result shows the biometric of ear can be used for forensic investigation and visual surveillance, but it is less suitable to be used for the authentication application, which needs a higher dependability. The main problem in the application of ear identification is in the process of the detection. As for the failure of ear detection is caused by the image of the ear that is too small caught by the camera or experiencing too much deformation of perspective. This detection problem can be solved by doing an increase in the resolution from the area of the targeted image, for example with the super resolution method.