Patent Application: US-201514687260-A

Abstract:
a method for rapid and robust one - step multi - factor authentication of a user is presented , employing multi - factor eye gaze . the mobile environment presents challenges that render the conventional password model obsolete . the primary goal is to offer an authentication method that competitively replaces the password , while offering improved security and usability . this method and apparatus combine the smooth operation of biometric authentication with the protection of knowledge based authentication to robustly authenticate a user and secure information on a mobile device in a manner that is easily used and requires no external hardware . this method demonstrates a solution comprised of a pupil segmentation algorithm , gaze estimation , and an innovative application that allows a user to authenticate oneself using gaze as the interaction medium and biometrics to authenticate an individual &# 39 ; s facial structure .

Description:
the user authentication system of the present invention achieves multi - factor authentication on a mobile device by challenging two identifying factors , knowledge and inherence . a mobile device may comprise a smartphone , tablet , laptop , smart watch , personal digital assistant , ultrabook , or any other intelligent portable device with , for example , a display , a camera , a programmed processor , and a user interface . the primary obstacles facing the implementation of either function are mitigated through the complementary arrangement of the algorithm &# 39 ; s flow . the knowledge factor allows the user to maintain the security of a password , and the biometric factor reduces the possible attacks that plague password systems . although the system operates and functions as a one - step system , several algorithms operate simultaneously to carry out the two factor procedure . the algorithm should be trained or calibrated to recognize and acknowledge only the user &# 39 ; s eyes , and in this way , only the user &# 39 ; s inputs will be received . this provides an extra level of security not present in current mfa approaches . this extra security , implemented in a fashion appealing to users , will be essential to fulfill the principal goal of this work — replace the password . to promote adoption of this method , the experience of existing password interfaces will be preserved to the utmost , with the exception of the interaction medium . the user selects a personal identification code or number ( pin ) composed of a sequence of any number ( above a specified minimum ) of digits , letters , shapes , colors , images , or other elements arranged on a screen , and an integrated camera provides images to a gaze estimation algorithm . once the gaze point is established , an estimation algorithm projects the gaze point onto the device &# 39 ; s screen , enabling the user to interact with the device and enter the pin expressed as a sequence of blocks occupying specific positions . as an added layer of security , random input feedback is given to the user until authentication is complete . the random input feedback is provided through colored blocks that shuffle on the screen when an input is received . using this approach , the user must rely on the phone to accurately estimate the gaze position , but the vulnerability of a malicious user observing the password is all but eliminated . this would require remote estimation of gaze point on the screens . furthermore , this method capitalizes on the advantages of combining knowledge and biometric factors and mitigates many of the disadvantages of using either knowledge or inherence factors exclusively . in providing a competitive replacement method of user authentication to a mobile device , a crucial design consideration is optimized implementation with respect to authentication accuracy , battery consumption , and duration for existing mobile platforms . all of the algorithms must be performed by a mobile optimized processor , limit unnecessary battery use , and operate using the integrated camera . additionally , the flexibility of mobile devices allows a user to be in any environment . ideally , the user would always be in the exact same environment with the same lighting conditions as those used for training the algorithm , however , this is not a reasonable assumption , so the detection and tracking algorithms must also address real - time issues such as non - static devices , inadequate lighting , and background image noise presented by ultrabooks , tablets , and smartphones . real - time video images from the device &# 39 ; s integrated camera are to be processed for the extraction of images of the eyes , which are passed to the gaze estimation and recognition phases . fig4 describes the flow of the algorithm . the first step of the algorithm is image acquisition 410 by the camera , followed by face detection 420 , then eye detection 430 , then pupil tracking 440 . iris capture and scanning techniques are a known method for performing biometric authentication and processing images of a user &# 39 ; s eyes , but are not yet practiced on mobile devices . it is not currently feasible to implement iris scanning techniques on typical mobile platforms because it requires specialized hardware not available on a standard mobile device . however , it is anticipated that hardware capable of performing iris scanning techniques will become a commonly available feature of mobile devices in the future . if iris scanning on mobile devices becomes feasible , it will be an obvious option to use iris scanning as part of this invention for iris and pupil detection and eye gaze tracking . the initial hurdle to establishing ocular movements as a viable method for users to interface with their mobile devices centers on reliable detection of not only the ocular region , but the finer details of the region as well . existing methods of gaze estimation rely on high resolution images and an infrared light source , but this invention aims to use the existing cameras integrated into mobile devices at the time of this writing , namely the user - facing cameras found in mobile devices . as these cameras are designed for transmitting video for video chat applications , the design emphasis of these cameras is the capture of low resolution images with a large field of view . haar cascades have been used in training and are used for detecting the user &# 39 ; s face and eyes . in over 1000 runs during the development of this work , these cascades allow the face and eyes to be detected rapidly and reliably . the detection time using the cascades is directly proportional to the number of pixels in , or size of , the image , so reducing the image size that is passed to the feature detection algorithm greatly reduces the detection time . optimizations are made so that the size of the image is reduced whenever possible before it is passed onto the subsequent processing stages . vertical face alignment is assumed throughout the authentication process to standardize the feature detection . fig5 illustrates an image of a user with the face and an eye image detected using the respective haar cascade files , with the larger square 520 showing the user &# 39 ; s face area as detected the algorithm , the smaller square 510 showing the user &# 39 ; s eye area as detected by the algorithm , and a circle 530 showing the user &# 39 ; s pupil as detected by the algorithm . referring to the algorithm depicted in fig5 , first , a scaled image ( 640 × 480 p ) along with the face cascade is passed to the feature detection algorithm to detect the face . the feature detection algorithm returns rectangles containing any areas in the image identified by the cascaded face filter . the best match is selected according to appropriate size and matching confidence . the user &# 39 ; s face is the first feature to be detected by the algorithm . if there are multiple faces in the image , the largest face in the image will be selected , with the assumption that the device will be decisively closer to the user who is trying to authenticate and would have the largest face in the images . the face detection portion of the algorithm returns the four points that form the corners of a rectangle which marks the face region . this face image can still be relatively large ( 400 × 300 p ), so , in order to speed up the eye detection , a mask is placed on the face image . the mask is created by sub - sampling the face image by half vertically and horizontally . then this half - sized image is passed to the eye detection step of the algorithm . optimizing for the human anatomy , the eyes are found above the horizontal mid - line of the face , and one eye is located on either side of the vertical mid - line of the face . the same matching algorithm that yields the face image is again used to apply the eye cascade to the input image . similar to the face detection algorithm , a special eye haar cascade is used to detect the specific eye region in the image depicted in fig5 . the top left corner of this region is particularly important , because it is used in the gaze estimation portion of the algorithm as well . while the algorithm detects an eye within the subdivided face region , the algorithm continues using the same face region . this step also greatly reduces time between image grabs , allowing the method to execute at standard video frame rates ( 20 - 30 frames per second ). this region of the image is cropped and passed to the pupil tracking portion of the algorithm . before the pupil can be tracked , it must first be segmented from the image . in image processing , segmentation refers to the separation or identification of all pixels corresponding to a specific object , in this case , the pupil . this will be accomplished through rudimentary image processing operations , in the hopes of keeping the computation time as low as possible . many extraction algorithms were explored during the initial stages of this work to establish the optimal segmentation method . the android operating system was chosen as a starting place to begin developing gaze - based multi - factor authentication mobile devices . the android software development kit ( sdk ) uses the eclipse development environment with the android developer tools plug - in installed . opencv , a library of programming functions written in c / c ++ aimed at real - time computer vision , has been ported to the android platform and implemented by the opencv4android library . opencv4android is released under a bsd license and gives android applications access to the opencv api by linking to the c library at runtime . initial work was targeted at demonstrating feasibility of accurate detection of a face , eyes , and eye details of a user with the mobile device within an arm &# 39 ; s length . the opencv4android library supports haar cascade feature detection , and , as such , lends itself well to the purposes of this work . the application developed for the android platform used the opencv feature detection library to detect features in images based on a haar cascade file . the images are acquired through the smartphone &# 39 ; s forward facing camera . this integrated camera usually is designed with video chat applications in mind , and has a lower resolution imager better suited for real - time processing . the application is straightforward and is designed to evaluate the feasibility of a smartphone &# 39 ; s hardware to implement a real - time feature detection application . the application triggers the smartphone &# 39 ; s camera to capture an image , and the image is passed to two feature detection steps . using the method previously outlined , the first step calls a feature detection function that uses the face haar cascade and returns an array of rectangles that contain facial components . the largest face rectangle is chosen , and the image is cropped to the rectangle of that face . this cropped and subsampled image is then passed to the eye detection function . along with the face subimage , an eye haar cascade file is passed to the function . as before , the function returns an array of rectangular regions that correspond to rectangles that bound the components of the eye . after the best eye region has been returned , all of the rectangles are drawn on the screen , a new image is captured , and the detection process is repeated . fig6 shows the rectangles that are detected from the application and displayed on the smartphone &# 39 ; s screen . fig6 depicts a screenshot of the android application with an image of the user marked with a larger rectangle corresponding to the user &# 39 ; s face as detected by the application , two adjacent smaller rectangles corresponding to the user &# 39 ; s eyes as detected by the application , and zoomed in images of each eye . with a static person , static device , and lighting conditions providing contrast for feature detection and gaze processing , the detection algorithm yields relatively accurate results . the goal in processing the eye region is to yield enough detail to authenticate the user and estimate the user &# 39 ; s gaze direction and sequence . the first step in the evaluation is to compile a database of images that represent a diverse user population . an image acquisition script , written in simplecv , was created to automatically save the images that are generated by the eye detection algorithms . simplecv employs the functionality of the opencv libraries using python wrappers to give developers a way to rapidly prototype image processing applications . along with current image processing support , the simplecv libraries also have webcam support , which allows real - time applications to be developed on computers without much initial setup overhead . unfortunately these features come at the cost of execution time , which increases proportionately to the resolution of the images being captured . however , video frame rates can still be achieved with optimized and resourceful coding . using scripts to automate the image acquisition process , a diverse eye database was established to allow processing techniques to be developed that would extract the pupil location from the eye of the diverse images . since the simplecv library and opcncv4android library both link to the opencv library , this allows an ultrabook to capture images comparable to what can be achieved by the smartphone . the ultrabook used for this work is an apple macbook air , with a dual - core 1 . 7 ghz intel core i7 processor and 8 gb of random access memory . the same feature extraction algorithm is employed from the android application , but this time , the cropped images of the detected face and eyes are saved as files , so that any language can interact with them . the eye image files were loaded into a database , since the organization of the images is important to determine the results of each segmentation method . fig7 shows an example of the image quality and resolution of the eyes captured by an ultrabook &# 39 ; s camera and contained in the database . to ensure that the database represents a substantial number of eye presentations , over 325 eye images were collected from ten different subjects in five independent lighting conditions . the images are stored according to the subject and lighting fields in the database . no groundtruth information for the images is stored in the database . although iris color is a relatively unique attribute , users were chosen based on distinctness of iris color . lighting conditions were chosen based on type of lighting ( incandescent , fluorescent , sunlight , etc .) and lighting angle ( overhead , ambient , structured , etc .). organizing this database by iris color and lighting , several eye processing techniques could be developed and rapidly tested on images of eyes to identify challenging combinations of iris color and lighting . the iris of the eye is segmented from the eye image in order to find the location of the pupil . given the high contrast edge between the sclera and the iris of the eye , an edge based approach was initially deemed the most favorable . the iris and pupil areas are assumed to be concentric circles . matlabqr was chosen as the development language , since the images from the database can be loaded by any matlabqr script . for the initial implementation , images are processed at 960 by 1280 pixel resolution . this gives the processing algorithms sufficient information to detect facial features and track the pupils , while not inhibiting the experience for the user . the resolution is an important consideration , because a subject &# 39 ; s eyes will likely represent a small portion of the pixels in each image , so the highest resolution that can be supported without reducing the frame rate is used . in order to maximize the frame rate it is important to find the algorithm that presents the greatest potential to accurately and quickly calculate the center of the iris within the eye image . for this work , three methods were evaluated to determine their fitness for pupil segmentation using the sample images in the eye image database : k - means clustering , daugman &# 39 ; s integrodifferential operator , and morphological processing . clustering techniques are commonly used in image processing and computer vision applications to group pixels in an image based on similar features , usually color or intensity . in k - means clustering , k optimal clusters result , and the pixels of an image are classified to a cluster with respect to the minimum distance in color between each pixel and the average color of the closest , most similar cluster . this method was chosen because of the perceived distinctness in color of the different components of an eye image — skin , iris / pupil , sclera / whites . the purpose of the k - means color - based segmentation method is the extraction of the colored iris region , containing both the iris and the pupil from the eye image . before applying k - means , the colorspace of the image is transformed , allowing a stronger and more perceptual representation of the color content in the image . the eye image is first converted from the red - green - blue ( rgb ) colorspace to the lightness - alpha - beta ( lab ) colorspace , where the alpha channel loosely corresponds to the red - green axis and the beta channel loosely corresponds to the blue - yellow axis . the alpha and beta channels are then clustered using k - means . in this algorithm , the pixels of the eye image are grouped into k different components according to euclidean distance between pixels , clustering the pixels that have the most similar color composition . this method operates under the assumption that three distinct color combination regions will be found ( skin , iris / pupil , sclera / whites ). due to this , the eye images were clustered using k equal to three , and the pixels of the iris and pupil are found in the cluster with the lowest magnitude . acceptable results can be expected in specimens where the skin is a noticeably lighter hue than the iris and pupil , but there are certainly cases where the iris and pupil may be lighter than the skin , due to either lighting or biology . fig8 shows results of the k - means clustering method with k = 3 using representative images from the database . the images result from treating the k = 3 segments of the original image as distinct images , and are sorted in ascending order of intensity . specifically , the segment with the lowest average intensity ( the “ darkest ” image ) is in the first column of images . visualizing the distinct segments validates the assumption that in well - suited specimens , the iris / pupil area of the image can be adequately segmented from the skin hue , since the darkest cluster of the image is assumed to be the iris / pupil . after applying the k - means color - based segmentation method to several eye images with k = 3 , 4 , 5 , a more discriminating approach based on physiological assumptions was chosen . observing the physiology of the human eye , the edge of the iris can be seen as a circular area of dark iris pixels bounded by an area of lighter pixels of the whites creates an edge . the goal of daugman &# 39 ; s integrodifferential operator is to fit a circle to the boundary of the darker circular area of the iris , yielding a center and radius of the circle . after the boundary information is obtained , the iris can be easily segmented as all pixels inside the circular boundary with the associated center and radius . daugman &# 39 ; s integrodifferential operator is an exhaustive search algorithm that finds the boundary between the iris and whites of the eyes . the operator searches over circles of all radii at each given center for the maximum average intensity gradient from across each concentric circle boundary to the next , along the radius and the center of the circles . the operator is applied throughout the region of interest ( roi ), and a gaussian blur may be applied to smooth out any outlier noise that may cause erroneous results . the complexity of the algorithm is quite high since every pixel is observed r times , where r represents the number of radii to be processed , or once for every radius in the range between the minimum and the maximum radius . for every radius in the specified range , the normalized sum of the intensities of all circumferential pixel values is calculated for every pixel acting as a center . for every radius increase , the difference between the normalized sums of pixel intensity values of the adjacent circles is stored . after processing the entire range of radii , the center of the circle yielding the greatest edge is stated to be the center pixel of the iris , the boundary of which has the greatest change in circumferential pixels . radman , juwari , and zainal present the algorithm implemented for this application in radman et al ., fast and reliable iris segmentation algorithm , iet image processing , 7 ( 1 ): 42 - 49 ( 2013 ). according to radman &# 39 ; s algorithm , the operator is governed by the following equation where 1 ( x , y ) is the intensity at coordinates x , y : r is the radius of the circular region with the center at x0 , y0 ; σ , held constant at 2 , is the standard deviation of the gaussian distribution ; s is the contour of the circle given by ( r , x0 , y0 ) governed by the equation of the circle : since every pixel in the image is a potential center candidate , preprocessing steps can help mitigate long processing times . in fact , several assumptions are valid , which can greatly reduce the candidate locations for the pupil center . it is assumed that the center of the pupil will be dark ( intensity value less than 50 ). this means that the only pixels passed to the algorithm will be those above a specified threshold intensity value . unfortunately , lighting conditions can create a glint reflection off the eye , creating the potential where the center of the eye may not be passed to the algorithm as a result of the center pixel being left out of the algorithm . for this reason , any glints caused by incident or directed lighting of the cornea are filled . additionally , some mathematical operations , such as division , can be avoided if the neighbors of the dark pixels are observed to ensure that only the darkest pixels in the neighborhood are passed to the algorithm . finally , it is assumed that the pupil is reasonably centered within the image , such that the best circle fitting the iris will never go outside the bounds of the image . referring to fig9 , the left column shows three original eye images captured by a mobile device &# 39 ; s camera , the center column depicts the result of daugman &# 39 ; s operator applied to the original color images , and the third column depicts the result of the same algorithm applied to black and white versions of the original images . in the second and third columns , the outer circle indicates the algorithm &# 39 ; s approximation of the corners of the eye , and the inner circle indicates the detected edge of the iris . as shown in the center column of fig9 , the added dimensionality of the color images adds more information to the edges and allows the algorithm to detect the true iris edge more accurately . however , this advantage illustrates the sensitivity of the algorithm &# 39 ; s performance to color . fig1 shows the erratic behavior of the algorithm when the iris is not in high contrast to the whites of the eyes . the left column of fig1 depicts original eye images captured by a mobile device &# 39 ; s camera , the center column depicts the result of daugman &# 39 ; s operator applied to the original color images , and the third column depicts the result of the same algorithm applied to black and white versions of the original images . as in fig9 , the outer circles represent detected edges of the eyes and the inner circles represent detected irises . in spite of the strong analytical validity of daugman &# 39 ; s integrodifferential operator , this method does not achieve the appropriate results in some cases due to the averaging in the integration part of the algorithm . as is evident by the results shown in fig1 , the intensity values of noise in the skin can create an average differential that mimics the average differential of an eye edge . additionally , the eye images do not present favorable data to the algorithm . the computational load that the operation requires is not well - suited for low performance processing in real - time environments . due to the real - time operational requirements of the solution and the low - power processor , image resolution and ambient lighting present very real challenges to the implementation of pupil detection in natural light settings . an overlooked aspect of the eye image is the eyelashes , and occasionally the eyebrow , that are sometimes included in an eye image . daugman &# 39 ; s operator does not properly handle partially occluded irises due to the eyelashes . eyelashes can cause a difficult situation where the irises are no longer detected , as the eyebrows may be in higher contrast to the whites than the iris . refocusing on algorithms that fulfill real - time constraints associated with image processing points to a solution employing rudimentary methods that have been coded and optimized in the simplecv library . given the need for deterministic performance when extracting biometric information , a method with strong analytical integrity was initially sought out . after encountering obstacles with two deterministic approaches to the iris segmentation , developing a real - time segmentation approach became the main priority . morphological segmentation uses nonlinear image filters , such as thresholding , dilation , and erosion . for this application , filters are selected that remove almost all information in the image except those pixels in the image representing the iris and pupil . although this approach offers no theoretical guarantees regarding optimal segmentation , it successfully segments the iris area in real - time a high percentage of the time . this method is comprised of three simple processing techniques , implemented on every image that is taken , usually accurately yielding the center of the user &# 39 ; s pupil when performed in sequence . the techniques described in this section are implemented using the same simplecv library that provides the feature detection . this allows the techniques to be seamlessly incorporated into one cohesive application that carries out the entire iris segmentation process , from image capture to identifying the center of the iris , whereas the previous methods would require intensive porting efforts . the first step in segmenting the iris area is reducing the eye image to a binary representation using an adaptive threshold . since the pupil should be the darkest region in the image , this binary representation separates the image into two categories : ( 1 ) pixels of intensity above the threshold and ( 2 ) pixels with intensity below the threshold . the threshold must be calibrated by the user from observed lighting conditions in the given setting to provide accurate results . future may be undertaken to develop a method for automated threshold selection . in the binary representation , the pixels that are below the threshold are classified with value 1 , with all other pixels being ignored and classified with value 0 . the output of the thresholding is shown in fig1 b , with the original image to which the thresholding was applied depicted in fig1 a . after the thresholding , the binary image contains several binary regions comprised of the dark pixels from the image , including several noise artifacts that must be removed before the center of the iris can be calculated . to remove the remaining noise regions , a morphological erosion filter is applied to the image , removing sporadic noise elements of the skin and glares or glints in the eyes . the erosion operator removes pixels or regions of the binary image that do not have sufficient area to be the iris . the erosion operator is applied with a 3 × 3 mask , dictating that the minimum area of the iris region must be greater than nine pixels . all binary regions with less than seven neighbors are eliminated from the image . since the edge pixels of the iris satisfy the elimination criteria for the erosion operator , those pixels must be restored after the erosion by applying a dilation filter to grow the areas . dilation reconstitutes the regions of the image that still remain , and attempts to grow connected regions of the image . the results following both morphological processing steps are illustrated in fig1 b , with intermediate thresholding results to which the second step was applied depicted in fig1 a . after the noise has been filtered out and the legitimately dark regions of the image are restored using morphological processing , the largest remaining region in the binary image should correspond to the iris region in the original color image . the final stage in segmenting the iris area from the eye image is calculating the center of the iris area to be used in estimating the user &# 39 ; s gaze . the simplecv blob detection operator is used to calculate the center of the largest connected component in the image . the blob detection method returns a list of regions in descending order of area , so the first region , i . e . the region of largest area , is chosen . the method also provides the centroids of all of the regions . fig1 a depicts the result of the eye image processing with blob detection and centroid calculation , and indicates the detected center of the iris area with a lighter gray circular target in the center of the image . fig1 b depicts the original color image of the eye with the detected center of the iris marked with a lighter gray target , displaying notably accurate performance . using rudimentary image processing , real - time segmentation of the pupil can be achieved . while the method suffers from sensitivity to light and requires tuning , the performance of this simple algorithm is notably superior to the previous , more complex methods . the next development stage centered on the creation of the application visible to the user during authentication . fig1 summarizes the performance of the three implemented algorithms in the presence of varying conditions . the variables concern user features and lighting conditions of the eye subimages in the database . all performance measurements are judged manually by the eye ten to fifteen samples , and are indicated by the poor , good , and best states . poor performance indicates less than three successful segmentation attempts averaged over ten attempts . good performance is achieved with more than five successful segmentation attempts , while best performance status is noted when more than eight of ten attempts are successful on average . comparing the performance of the iris segmentation algorithms , the results show that the morphological processing approach performed most favorably . the thresholding step allows the method to adjust to varying lighting conditions . even so , lamp and dim lighting are the harshest conditions for all of the methods . these lighting conditions do not provide the necessary illumination to allow confident segmentation of the eye images . interestingly , overhead lighting casts a shadow on the user &# 39 ; s eyes and causes a loss of contrast , reducing the accuracy of daugman &# 39 ; s algorithm . light irises also pose a harsh challenge for the methods to deal with , due to the lack of contrast between the iris and the whites of the eyes . to mitigate this effect , the morphological processing approach is still able to segment the pupil area as this will usually be a dark area , with the exception of bright directed lighting . an intense glint may be reflected in the presence of bright directed lighting , causing the pupil region to have high intensity values instead of low intensity values . the perfect user environment would be a user with light skin and dark irises in an overhead lighting condition . this situation consistently provides the best results when applying morphological processing to the eye image database and during real - time operation . this section describes an application that has been developed to carry out the present invention &# 39 ; s authentication method using multi - factor eye gaze . the application performs its tasks in three phases . the first phase implements the calibration needed for the application to deliver accurate performance . the second phase requires the user to establish a personal identification code ( hereinafter referred to as “ pin ” for simplicity but not limited to a sequence of numbers , as the personal identification code can be composed of any sequence of elements that can be displayed on the device &# 39 ; s screen ) of user selected length for use in all subsequent authentication attempts . the third phase of the application allows users to securely enter their pins using multi - factor eye gaze . fig1 shows an embodiment of the application &# 39 ; s user interface , which was developed according to the design and requirements detailed above . to enter the pin or password , the user must interact with the device using eye gaze . in the embodiment depicted in fig1 , twelve colored blocks are presented , by way of example , to the user , arranged in four rows and three columns , also by way of example . other arrangements of the interface with different numbers of objects and different types of objects ( which may include symbols , different shapes than blocks such as circles , colors , numbers , letters , words , images , patterns , etc .) can be used and are equivalent . each block plays the role of a symbol , corresponding to its position , in a pin . as the user &# 39 ; s gaze settles on a chosen block for a sufficient amount of time ( approximately 400 ms , for example , a range between 300 ms and 1 second ), an input to the device is triggered . the colors of all of the blocks randomly change when an input is recognized , indicating to the user that an input has been received and the device is ready for the next input . the application was developed using pygame , the python gaming library . it supports basic interface capabilities that were integrated into the existing pupil segmentation application . the pupil segmentation application outputs the center of the pupil , the user interface provides feedback to the user , and the gaze estimation framework translates the pupil center into a gaze point on the screen for the user interface . before implementation , the center of the eye region , depicted in fig1 as an eye shaped image in the center of the screen , in the second column between the second and third rows , is first established to serve as the reference point of a neutral gaze . this point will be utilized during an authentication attempt to assess the direction of the user &# 39 ; s gaze . if the subject maintains a neutral gaze by looking at the middle of the mobile device , the center of the pupil and the center of the eye region should be roughly the same . to estimate the point in space on the screen where the subject is gazing , the two - dimensional difference ( δx , δy ) between the reference center of the eye region and the pupil center is used . the output of the pupil segmentation step is the center of the extracted pupil segment . the center of the eye region can be calculated as the center of the eye subimage . it is important to remember that these points are not identical . for this reason , the center of the eye region is used as the reference point of a neutral gaze , to be taken and stored into the memory of the device . for most subjects , the two centers will not align perfectly , so a translation constant must be calculated to offset the reference point or eye region center . if the subject looks away the eye region center and the pupil center are no longer at the same position , and the distance between these points is measured . the distance measurement is provided in its horizontal , δx , and vertical , δy components as a two - dimensional vector called the gaze vector , ( δx , δy ). through calibration steps , centroids for each block on the interface are then established to classify measured differences and represent estimated gaze point as screen coordinates . as opposed to requiring a calibration step for each block independently , resulting in twelve steps , vertical and horizontal centroids are calibrated by two independent calibration steps . the first step establishes the four vertical centroid points by prompting the user to gaze at each of the four central blocks along the vertical axis and averaging the vertical components of the gaze vectors across multiple samples , as shown in fig1 a . the second step calibrates the three centroids across the horizontal axis , as shown in fig1 b using the same method , resulting in seven calibration stages rather than twelve and reducing calibration time . after calibration is complete , every gaze vector that is sampled from a new image is classified according to the closest centroid in each direction . classification of the gaze vector in this manner achieves the gaze estimation , and enables the application to perform the biometric portion of the authentication , but the knowledge factor remains to be established . after the calibration of the gaze estimation is complete , the application enters the next phase . during the second phase of the application , the user establishes the length and value of the pin to be used in all subsequent authentication attempts . this setup phase is required once . the user first chooses the length of pin to create , with longer pin selections providing more security and longer input times . the user selects among the range of lengths from four to seven symbols ( more symbols should be used in a fielded system . after the length is chosen , the user creates the pin that will be used for authentication . creating the pin is achieved through eye gaze to acclimate the user to the new interface . once the pin is established , it is important that the pin be stored into the device &# 39 ; s secure and encrypted place in memory to protect it from any malicious memory attacks . this allows the pin to be used securely during the third application phase until the user decides to manually recreate the pin . in the application &# 39 ; s third phase of pin entry , multi - factor authentication using eye gaze may be performed seamlessly . the user , when prompted with the authentication screen , gazes at the necessary positions of the blocks in sequence that represent the proper value to enter in the correct pin established during the second phase . if the biometric features of the user &# 39 ; s eyes do not correspond to the calibration established during the first phase , the application will not be able to authenticate successfully . similarly , if the application recognizes the user &# 39 ; s input , but the entered pin is not the same as the one stored in the encrypted memory , authentication will fail . only when both the biometric and the knowledge criteria are met will the user be able to successfully authenticate . through testing of the application , users other than the user who performed the calibration steps were rejected , and false positives were never encountered . unfortunately , genuine users attempting to authenticate experienced false negative results . this indicates a high sensitivity of the biometric recognition portion of the application to something other than the user and the password and has been identified as future work . since the gaze estimation of the application is based on the morphological segmentation algorithm , the performance of the application is subject to the same limitations as the morphological segmentation algorithm , namely the lighting conditions . as a result , authentication attempts using the application must be performed under lighting environments similar to the calibration environment . further embodiments of the present invention include automatic compensation for lighting conditions to increase the performance of the algorithm . in one embodiment , the invention is directed toward one or more computer systems such as mobile devices capable of carrying out the functionality described herein having associated memory and databases . an example of a computer system 1700 of a sophisticated intelligent mobile device is shown in fig1 . the example does not show all aspects of a mobile device such as the camera , a clock , a time of day and date calendar , a gps unit , an accelerometer and other features of a typical mobile device . however , such features of an ever improving digital camera are typically found in mobile devices known in the art and even comprise video cameras for capturing sequences of images if selected by a user . computer system 1700 includes one or more processors , such as processor 1704 . the processor 1700 is programmed as a special purpose processor to authenticate a user using biometric ( for example , facial structure ) and a personal identification code entered by the user each time a mobile device is turned on and prepared for use by an individual user . the processor 1704 is connected to a communication infrastructure 1706 ( e . g ., a communications bus or network ). various software aspects are described in terms of this exemplary computer system . after reading this description , it will become apparent to a person skilled in the relevant art ( s ) how to implement the invention using other computer systems and / or architectures . users of mobile devices ( not shown ) communicate with computer system 1700 by means of communications interface 1706 , typically a touchscreen having a reprogrammable display or other interface known in the art . a typical mobile device computer used by a user may have a similar structure to computer system 1700 , the difference being that computer system 1700 may comprise databases and memory . a mobile device , on the other hand , provides a user with access to any of these for creating new images or doing any of the creation of the images and image portions such as face , eye region and pupil as discussed above . computer system 1700 can include a display interface 1702 that forwards graphics , text and other data from the communication infrastructure 1706 for display on the display unit 1730 . a display , as will be described herein , may provide a touch screen for , for example , entering data . computer system 1700 also includes a main memory 1708 for maintaining the authentication and image processing algorithms described above , preferably random access memory ( ram ) for temporary data storage and may also include a secondary memory 1710 . the secondary memory 1710 may or may not include , for example , a hard disk drive 1712 and / or a removable storage drive 1714 , representing a floppy disk drive , a magnetic tape drive , an optical disk drive , etc . the removable storage drive 1714 reads from and / or writes to a removable storage unit 1718 in a well known manner . removable storage unit 1718 represents a floppy disk , magnetic tape , optical disk , micro sd card , etc . which is read by and written to by removable storage drive 1714 . as will be appreciated , the removable storage unit 1718 includes a computer usable storage medium having stored therein computer software and / or data . in alternative aspects , secondary memory 1710 may include other similar devices for allowing computer programs or other code or instructions to be loaded into computer system 1700 ( for example , downloaded upon user selection from a server ). such memory devices may include , for example , a removable storage unit 1722 and an interface 1720 . examples of such may include a program cartridge and cartridge interface ( such as that found in some video game devices ), a removable memory chip ( such as an erasable programmable read only memory ( eprom ), or programmable read only memory ( prom )) and associated socket and other removable storage units 1722 and interfaces 1720 , which allow software and data to be transferred from the removable storage unit 1722 to computer system 1700 . computer system 170 also includes a communications interface 1724 which may be a cellular radio transceiver known in the cellular arts . mobile communications interface 1724 allows software and data to be transferred between computer system 1700 and external devices and may comprise access to telecommunications , texting , the internet , social networks , movies via netflix , games and the like but only after authentication . as discussed above , a biometric and personal identification code multi - factor gaze authentication is presented for use with obtaining access to such device features . examples of communications interface 1724 may include a modem , a network interface ( such as an ethernet card ), an rf communications port , a personal computer memory card international association ( pcmcia ) slot and card , etc . software and data transferred via communications interface 1724 are in the form of non - transitory signals 1728 which may be electronic , electromagnetic , optical or other signals capable of being received by communications interface 1724 . these signals 1728 are provided to communications interface 1724 via a telecommunications path ( e . g ., channel ) 1726 . this channel 1726 carries signals 1728 and may be implemented using wire or cable , fiber optics , a telephone line , a cellular link , an radio frequency ( rf ) link and other communications channels . in this document , the terms “ computer program medium ” and “ computer usable medium ” are used to generally refer to media such as removable storage drive 1714 , a hard disk installed in hard disk drive 1712 and signals 1728 . not all intelligent mobile devices have all these features . these computer program products provide software to computer system 1700 . the invention is directed to computer authentication methods and apparatus . computer programs ( also referred to as computer control logic ) are typically stored in main memory 1708 and / or secondary memory 1710 . computer programs may also be received via communications interface 1724 . such computer programs , when executed , enable the computer system 1700 to perform the features of the present invention , as discussed herein . in particular , the authentication computer programs of the present invention , when executed , enable the processor 1704 to perform the features of the present invention and provide access to further features that are virtually unlimited ( but importantly , personal to a user individual and should not be accessed by others without permission from the user ). accordingly , such computer programs represent controllers of the computer system 1700 . in an embodiment where the invention is implemented using software , the software may be stored in a computer program product and loaded into computer system 1700 using removable storage drive 1714 , hard drive 1712 or communications interface 1724 . the control logic ( software ), when executed by the processor 1704 , causes the processor 1704 to perform the functions of the invention as described herein . the present authentication method and apparatus may be downloadable to a mobile device from an applications store . in another embodiment , the invention is implemented primarily in hardware using , for example , hardware components such as application specific integrated circuits ( asics ). implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art ( s ). as will be apparent to one skilled in the relevant art ( s ) after reading the description herein , the computer architecture shown in fig1 may be configured as any number of computing devices such as a system manager , a work station , a game console , a portable media player , a desktop , a laptop , a server , a tablet computer , a pda , a mobile computer , a smart telephone , a mobile telephone , an intelligent communications device or the like . all non - patent literature , u . s . patents and u . s . published patent applications cited herein and below in the bibliography should be deemed incorporated by reference as to their entire contents for any purpose . a bibliography of source 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