Ear Biometrics for Machine Vision

A new class of biometrics based upon ear features is introduced for use in the development of passive identi cation systems The viability of the proposed biometric is shown both theoretically in terms of uniqueness and measurability over time and in practice through the implementation of a proof of concept system Identi cation by ear biometrics is promising because it is passive like face recognition but instead of the di cult to extract face biometrics it uses robust and simply extracted biometrics like those in ngerprinting The goal of automated personal identi cation that is both positive and reliable has attracted increased interest in automated biometrics Automated biometrics can be divided into two broad classes physiological which are based upon measurements of external physical traits and behavioral which usually measure learned behaviors carried out by the subject Behav ioral methods include signature voice and keystroke biometrics all of which vary over time and are dependent upon environmental factors Machine vision research has concentrated on physiological e g face hand eye and ngerprint biometrics Section one introduces the eld of automated biometrics and contrasts the success of machine vision research on invasive methods like ngerprint identi cation against its apparent weakness on passive methods like facial biometrics The viability of a new biometric based on ears and suitable for passive identi cation is argued in section two which examines the uniqueness and comparability over time of the ear The short history of the ear in manual biometrics and the features developed by the forensic scientist Iannarelli are given in section three Section four mathematically de nes identi cation and recognition while sketching our ear biometric identi cation system with emphasis on methods for ear localization Finally two application scenarios are given followed in the last section by our current research directions Introduction to Automated Biometrics Automating face biometrics has been extensively studied in machine vision see Chellappa for a survey Despite extensive research many problems in face recognition remain largely unsolved due to the inherent di culty of face biometrics A wide variety of imaging problems e g lighting shadows scale and translation plague the attempt for unconstrained face identi cation In addition to the many imaging problems it is inherently di cult to collect consistent features from the face as it is arguably the most changing part of the body due to e g facial expressions cosmetics facial hair and hair styling The combination of the typical imaging problems of feature extraction in an unconstrained environment and the changeability of the face explains the di culty of automating face biometrics Despite the attractiveness of face biometrics e g they are easily veri able by non experts of biometrics e g ngerprint based provide the basis for most commercial implementations Unlike facial biometrics ngerprint based biometrics have been shown to be highly amenable to automation by machine vision techniques The automation of ngerprint biometrics began in and has culminated in a number of commercial machine vision based systems In terms of traditional image processing problems ngerprint imaging is done within a controlled environment usually a specially designed scanner which eliminates the problem of localization and artifacts from shadowing and lighting variations Physical changes a bane of facial biomet rics is a miniscule problem as the nger baring surgery remains comparatively constant over time Machine vision techniques have been applied successfully and have provided highly accurate and robust commercial systems which are in use worldwide Fingerprints are not the only successful example of the application of machine vision techniques to automated biometrics but both the three dimensional shape of the hand and retinal patterns have also been used All of the biometrics which have been successfully automated using ma chine vision techniques are inherently invasive They require the subject to participate actively in both enrolling into the system and in subsequent identi cations The willing participation of the subject in the controlled environment of the machine vision systems is intrinsic in the success of the identi cation One class of passive physiological biometrics are those based upon iris scans Unlike retinal scans which require close contact with the scanner iris based recognition has been reported from distances of cm in controlled situations The unique collection of striations pits and other observable features of the iris along with the ease of segmenting the iris from the white tissue of the eye which serves as its background make iris based biometrics attractive The decided disadvantage is the small size of the iris which makes image acquisition from any distance greater then cm problematic To summarize the two classes of passive physiological biometrics which have been researched in machine vision up to now face and iris based techniques both have a number of drawbacks which make their usage in commercial applications limited Facial biometrics fail due to the changes in features caused by expressions cosmetics hair styles and the growth of facial hair as well as the di culty of reliably extracting them in an unconstrained environment exhibiting imaging problems such as lighting and shadowing Iris features on the other hand remain relatively consistent over time and are easy to extract but acquisition of the image at the necessary resolution from a distance is di cult Therefore we propose a new class of biometrics for machine vision based upon ears which have both reliable and robust features and are localizable and segmentable from a distance for passive identi cation Viability of Ear Biometrics In proposing the ear as the basis for a new class of biometrics we need to show that it is viable i e unique to each individual and comparable over time In the same way that no one can prove that ngerprints are unique we can not show that each of us has a unique ear Instead we will assert that this is probable and give supporting evidence by examining two studies The rst study compared over ears drawn from a randomly selected sample in California and the second study examined fraternal and identical twins in which physiological features are similar The evidence from these studies supports the hypothesis that the ear is a unique physiological features since in both studies all examined ears were found to be unique though identical twins were found to have similar but not identical ear structures especially in the concha and lobe areas Having shown uniqueness it remains to ascertain if the ear provides biometrics which are comparable over time It is obvious that the structure of the ear does not change radically over time The medical literature reports that ear growth after the rst four months of birth is highly linear i e proportional It turns out that even though ear growth is proportional gravity can cause the ear to undergo stretching in the vertical direction The e ect of this stretching is most pronounced in the lobe of the ear and measurements show that the change is non linear The rate of stretching is approximately ve times greater then normal during the period from four months to the age of eight after which it is constant until around when it again increases We have shown that biometrics based upon the ear are viable in that ear anatomy is probably unique in each individual and that features based upon measurements of that anatomy are comparable over time Given that they are viable identi cation by ear biometrics is promising because it is passive like face recognition but instead of the di cult to extract face biometrics it can use robust and simply extracted biometrics like those in ngerprinting