FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP

857.2 Eye color as a soft biometric traitIn this section we focus on eye color as a soft biometric trait, where we spot eye color information,previously mostly disregarded by classical iris pattern and texture recognition methods.We then specifically examine extraction and categorization of eye color and conduct an additionalstudy where we illustrate the influence of surrounding factors like illumination, eye glasses andsensors on the appearance of eye color.7.2.1 The human iris colorThe human ocular region bears a plethora of biometric traits with different importance, suchas iris, sclera and retina patterns, eye shape, eye brow shape and size and eye color. Eye colorhas been mainly neglected, probably for the reason that 90% of humans have brown eyes. Thedistribution of eye colors characterizing Caucasian and specifically European subjects is of a biggerdeviation than in the rest of the world, as an example of the German speaking region provesin [hai10]. Eye color is determined by the amount and type of pigments in the eye’s iris andis genetically inherited. It is of more permanent character than other soft biometric traits. Wenote though that ’hazel’ eye color is a special case, where it is known to change its colors. Astudy [BMC + 97] shows that eye color changes slightly over the span of 40 years. The extractionof eye color is of very sensitive character, since the area is small (around 11mm) and color itself isdifficult to deduce because of its illumination sensitivity. A further difficulty is the variable size ofthe pupil (mainly due to illumination), from3−4mm up to5−9mm. Then again, positive factorsfor the feasibility of eye color classification are the smaller and lower-priced surveillance sensors,which are increasingly available, and furthermore provide higher resolutions.7.2.2 Related workFew preliminary scientific works on eye color exist, as on subjectivity of human eye colorgrading [SSG + 90], [FCB08], on human analysis of eye color photographs [TSC + 01] and firstpreliminary classification attempts [MRGL00] and [FDH03]. We clearly differentiate our work,by presenting a full automatic eye color categorization system and furthermore by providing insighton related pertinent factors.We present here a preliminary study towards a robust eye color classifier. In Section 7.3 wedescribe an automatic eye color classification algorithm, which contains automatic iris extractionand Gaussian mixture models for classification. Simultaneously related results on the reliabilityare presented. Section 7.4 offers a preliminary study on factors with impact on eye color classification,such as illumination, camera sensor, presence of glasses and consideration of the left or righteyes. Such an eye color classifier can serve as a preprocessing step of an iris pattern classificationsystem, where we do not expect to increase the reliability of the overall system (iris patterns areconsidered as highly reliable biometrics), but our system rather can pre prune the database to savecomputational complexity and time, see Chapter 4 and [HJP99].7.3 Iris color classificationIn designing an automatic eye color classification system, the choice of the method for irisextraction as well as the color classification have to meet the criteria of reliability, of time and of