In this paper, we propose a new subsetting scheme for the longitudinal face aging databaseMORPH-II. Our subsetting scheme is intended to overcome the unbalanced racial and genderdistributions of MORPH-II, while ensuring independence between training and testing sets.Our subsetting scheme can be used for various face analysis tasks, including gender classica-tion, age prediction, and race classication

NC DOCKS at The University of North Carolina Wilmington

Wang, Yishi

English

The University of North Carolina at Greensboro

MORPH-II: A Proposed Subsetting SchemeParticipants: K. Kempfert, J. Fabish, K. Park, and R. TownerMentors: Y. Wang, C. Chen, and T. KlingNSF-REU Site at UNC Wilmington, Summer 2017AbstractIn this paper, we propose a new subsetting scheme for the longitudinal face aging databaseMORPH-II. Our subsetting scheme is intended to overcome the unbalanced racial and genderdistributions of MORPH-II, while ensuring independence between training and testing sets.Our subsetting scheme can be used for various face analysis tasks, including gender classifica-tion, age prediction, and race classification.1 IntroductionMORPH is one of the largest publicly available longitudinal face databases [5]. Since its first releasein 2006, it has been cited by over 500 publications, as determined by our Google Scholar search.Multiple versions of MORPH have been released, but for our subsetting scheme we use the 2008MORPH-II non-commercial release (which will be referred to as MORPH2008 here). MORPH2008includes over 55,000 longitudinal mugshots, taken between 2003 and late 2007. For each image, thefollowing metadata is included: subject ID number, picture number, date of birth, date of arrest,race, gender, age, time since last arrest, and image filename. Because of its size, its longitudinalspan, and its inclusion of relevant metadata, MORPH2008 has been used for a variety of race,gender, and age face imaging tasks. In addition to MORPH2008, we also briefly address a previousversion of MORPH-II, which has been used by face imaging researchers in the past. Such discussioncan be found in the following section.2 BackgroundOur subsetting scheme is motivated by Guo’s and Mu’s work in [2, 3, 4], which is done on aprevious version (with unknown date) of the MORPH-II noncommercial dataset, which here wewill refer to as MORPHpre. MORPHpre and MORPH2008 are very similar, but there are someminor differences. Most images are the same, but there are 2 images in MORPH2008 that are notincluded in MORPHpre. MORPHpre includes an additional race category Indian, in addition tothe 5 races in MORPH2008: White, Black, Asian, Hispanic, Other. Beyond the specific differencesmentioned, MORPH2008 is an updated, cleaner version of MORPHpre. This information is brieflysummarized in Table 1.For comparative purposes, the experimental design by Guo and Mu from [2, 3, 4] will be sum-marized first. About 66.75% of images in MORPHpre are of black males, while only about 4.72%of images are of white females. Guo and Mu utilize a subsetting scheme to overcome such dispro-portionate distributions of racial and gender groups in MORPHpre. They denote the whole datasetas W and randomly select a subset S of 21,060 white and black faces of both genders from ages 16to 67. Only white and black subjects are included in S, because other races have too few imagesfor use in the training set. In S, half the images are white, and the other half are black. There are1three times as many males as females in S. R denotes the set of remaining images, W \ S. In R,there are approximately 34,000 images including both genders, all 6 races, and ages 16-77 years.These subsets are summarized in Figure 1.Table 1: Differences between MORPHpre and MORPH2008Number of Images Race Categories Other QualitiesMORPHpre [2, 3, 4] 55,132 W,B,H,A,I,O older versionMORPH2008 55,134 W,B,H,A,O newer version; cleanerS is further divided equally into S1 and S2, with 10,530 images for S1 and S2, respectively. S1and S2 are used alternately for training and testing purposes. First, S1 is used for training andW \ S1 for testing. Then S2 is used for training and W \ S2 for testing. This process is similar to2-fold cross-validation, but S1 and S2 are not obtained by a random partition of the images in S;instead, S1 and S2 are controlled to be as similar as possible.Figure 1: This flowchart represents Guo’s and Mu’s subsetting scheme in [2, 3, 4].In S1 and S2, Guo and Mu ensure equivalent age distributions within gender. For example,the set of white females in S1, S1WF , has an identical age distribution to the set of black femalesin S1, S1BF . The equivalence of age distributions is summarized in Figure 2. Through this sub-setting scheme, Guo and Mu form training sets that are more proportionate in gender and race.Additionally, they guarantee identically distributed ages within each gender class.However, the training and testing sets are not necessarily independent. It has been found thata number of subjects belong to both S1 and S2 and, in some cases, even to R. For this reason,there may be information leakage between these sets. Consequently, any models built and validatedaccording to this subsetting scheme may suffer from inflated accuracy rates (or deflated error rates).2Figure 2: Age Distributions for subsets S1 and S2 in S are shown for Guo’s and Mu’s subsettingscheme [2, 3, 4]. On the top row of the figure, it is shown that S1WF , S1BF , S2WF , and S2BF haveidentical age distributions. The bottom row of the figure depicts the identical age distributions forS1WM , S1BM , S2WM , and S2BM .3 New Subsetting Scheme3.1 DevelopmentLike Guo and Mu, we only consider the white and black races for the training sets, since the numberof images for other racial groups is too small. In our subsetting scheme, we seek to retain Guo’s andMu’s ratios of white:black and male:female images, while ensuring independence between trainingand testing sets. We also prioritize randomization, aiming to create many candidate subsets fromwhich to choose. These possible subsets can be used for comparative purposes in the future; modelscould be built and validated on different subsets, and the results could be averaged or compared.Additionally, we use MORPH2008, since it is the most up-to-date version of MORPH-II. Further,we generate our subsets from a version of MORPH2008 that has been more thoroughly cleaned.The motivation for cleaning MORPH2008 is the detection of various inconsistencies in gender,race, and birthdates among repeat offenders in MORPH2008. Such inconsistencies are validated andcorrected. Note that a new variable age dec is also created to represent the exact age in decimal ofthe subject pictured in each image. Hereafter, those age values in decimal will be used in subsettingand future facial demographical study, since they are less biased than integer-valued ages. Thedecimal age values are also advantageous due to their improved continuity, which is essential as anassumption for the nonparametric tests discussed in a later section. More details on the cleaningof the MORPH2008 dataset can be found in [1]. The cleaned version of MORPH2008 from [1] isused for the generation of subsets in this study.For ease of comparison, we attempt to be as consistent as possible with the set notation in [2, 3,4]. Let W be the Whole cleaned dataset (morphII go for age), S be the main training/validationset, and R be the remaining set. We divide S into S1 and S2, such that S1 and S2 have the samenumber of images. We fix the ratios of white:black images to be 1:1 and male:female images to be3:1.Because white females are the smallest race-gender combination, we include all 2,570 whitefemales in S. We randomly allocate each white female subject to either S1 or S2 exclusively,3according to the constraint that the total number of white female images in S1 is equal to thenumber of white female images in S2:|S1WF | = 1285 = |S2WF |.Note that all white females are included in S, hence none are included in R.For the other race-gender categories (black females, white males, and black males), we includeonly a portion of their images in S, while the remainder goes in R. For black females, we randomlyallocate a subset of subjects to S1 and an exclusive subset of subjects to S2, such that the totalnumber of black female images in S1 is equal to the total number of black female images in S2, aswell as equal to the total number of white female images in Si, i = 1, 2. The images pertaining toany remaining black female subjects are sent to R.|S1BF | = 1285 = |S2BF | = |SiWF |, i = 1, 2.For white males, we randomly allocate some subjects to S1 and other distinct white malesubjects to S2, such that 3 times the number of white female images are in S1. The number ofwhite male images in S1 is also set to be equal to the number of white male images in S2. Anyremaining white males’ images are sent to R.|S1WM | = 3855 = |S2WM | = 3|SiWF | = 3|SiBF |, i = 1, 2.The same process is repeated for black males, so that there are equal numbers of black maleimages within S1 and S2. The number of black male images is equal to the number of white maleimages, and other equalities hold too:|S1BM | = 3855 = |S2BM | = |SiWM | = 3|SiWF | = 3|SiBF |, i = 1, 2.In this way, we ensure independence between S1, S2, and R. There is no expected informationleakage between the training and testing sets. However, it should be clarified that observationswithin each set are not independent. For each subject sj in some set Ω, all of sj’s images are inΩ. Hence, some observations within each set Ω are correlated with each other. Though we are ableto guarantee independence between training and testing sets, at this time we have no satisfactorysolution to the issue of correlated images within each set. This is an issue inherent to longitudinaldata.3.2 ImplementationWe implement our subsetting scheme in the statistical software R. We iterate through various valuesof k (k = 1, 2,...). Subsets are randomly generated for each value of k, with a random seed of k setanytime randomization is invoked. In this way, numerous candidate subsets are created.Among the candidate subsets, we seek those with similar age distributions. We obtain the agedistributions of images in S1 and S2. Then for each value of k, we perform both the Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) tests on those distributions. The hypotheses for bothtests are as follows:Ho = S1age has the same distribution as S2ageHa = S1age does not have the same distribution as S2ageWe use the P-Values of both tests to identify the best subsets. High P-Values indicate S1 andS2 have similar age distributions for a particular seed k. Hence, we use the P-Values for these4nonparametric tests as metrics for judging suitable subsets. In this context, the P-Values are not tobe interpreted as clear probabilities, for the following reasons: not all assumptions for the tests aremet (since some observations within each set are dependent) and the significance level cannot bedefined appropriately when an indefinite number of tests are made. We believe our unconventionaluse of P-Values is valid here, since we are not attempting to make any probability statements basedoff them.Using these criteria, we identify random seed number k = 42 as one which produces satisfactorysubsets. The P-Values for the KS and AD tests are 0.657 and 0.652, respectively. The statisticalsummaries below further indicate the suitability of these subsets. We do not guarantee that randomseed 42 produces the global optimum results, but it is found to be satisfactory for our purposes.In Tables 2, 3, and 4, we include basic information pertaining to the subsets generated by therandom seed 42. We intend the subsets W , S, and R to be used as Guo and Mu did in [2, 3, 4]:first the model should be trained on S1 and tested on W \ S1, then the model should be trainedon S2 and tested on W \ S2. Then two sets of results can be summarized.Table 2 shows the number of images in subsets by race and gender, while Table 3 gives thenumber of distinct subjects in subsets by race and gender. More detailed information on thedifferent races is summarized in Table 4, with the number of images and the number of distinctsubjects for the additional race groups in the remaining subset R. In Tables 2 and 3, d denotesdifferent race subjects (Hispanic, Asian, or Other).Table 2: Number of Images in Subsets by Race and GenderWF BF WM BM dF dM Overall F MS1 1,285 1,285 3855 3,855 0 0 10,280 2570 7,710S2 1,285 1,285 3,855 3,855 0 0 10,280 2,570 7,710R 0 3,150 220 28,980 144 1,850 34,344 3,294 31,050Overall 2,570 5,720 7,930 36,690 144 1,850 54,904 8,434 46,470Table 3: Number of Distinct Subjects in Subsets by Race and GenderWF BF WM BM dF dM Overall F MS1 311 332 1,005 948 0 0 2,596 643 1,953S2 313 336 988 943 0 0 2,580 649 1,931R 0 809 55 6,899 40 568 8,371 849 7,522Overall 624 1,477 2,048 8,790 40 568 13,547 2,141 11,406Table 4: Additional Race Groups in Remaining Subset RHF AF OF HM AM OM Overall F MSubjects in R 28 4 8 502 47 19 608 40 568Images in R 99 13 32 1,646 140 64 1,994 144 1,850Additional graphical and numerical summaries are presented in Figures 3, 4, 5, and Table 5.5Figure 3: For random seed 42, the observed age histograms in S1 are displayed.Figure 4: For random seed 42, the observed age histograms in S2 are displayed.Figure 3 displays the observed age histograms in S1. It is shown that all the gender-racecombinations in S1 have similar, right-skewed age distributions. Any differences in distribution hereseem minor and unlikely to significantly affect gender or race classification in future experiments.Figure 4 presents the observed age histograms in S2. Based on the plots, all the gender-racecombinations in S2 seem to be similarly distributed. Further, we see that the age distributions inS1 are not much different than the age distributions in S2. We do not expect any deviations in agedistribution between sets to negatively impact classification in a significant way.6Figure 5: The age distributions for images in S1, S2, and the W dataset are depicted.The age distributions for images in S1, S2, and the W dataset are depicted in Figure 5. It isshown that all three histograms are right-skewed with a roughly bimodal structure, indicating thatS1 and S2 have been chosen successfully; the age distributions of images in the subsets S1 and S2are close to the overall age distribution of images in W . The final plot shows the ECDFs of S1 andS2. It is difficult to distinguish the densities corresponding to each subset, since departures are sominor. This aligns with our expectations, because the P-Values for the KS and AD tests were quitelarge (approximately 0.65 for each).In Table 5, the 5-Number Summary, as well as mean and standard deviation, are given for agein S1, S2, and W . The statistical summaries are nearly identical, further confirming these subsets’balanced age distributions.Table 5: Numerical Summary of Age in SetsMin. Q1 Median Q3 Max Mean SDS1 16.003 24.296 34.495 42.185 77.196 34.041 10.957S2 16.005 24.370 34.371 42.014 75.421 33.926 10.908W 16 23.369 33.091 41.422 77.196 33.019 10.950All numerical and graphical summaries we consider here indicate the suitability of the subsetsgenerated from random seed 42. These subsets are expected to yield good results for a variety offace imaging tasks, including gender and race classification, as well as age regression.4 ConclusionIn our paper, we propose a subsetting scheme of the MORPH-II aging database. Our scheme isinspired by the work of Guo and Mu, but we do make some changes. Most notably, we maintain theracial and gender proportions of Guo and Mu, while ensuring independence between training andtesting sets. Our approach is also novel in its generation of various candidate subsets, which areselected based off nonparametric goodness of fit tests KS and AD. We present one suitable choice7of subsets for a random seed of 42, but the generation of other subsets from the random seeds k arerecommended for comparative purposes in the future. For any models built and tested using thesubsetting scheme proposed in this study, it is expected the estimates of test error or accuracy canbe less biased. Our subsetting scheme can be used for face imaging tasks involving gender, race,and age.5 AcknowledgementsThis material is based in part upon work supported by the National Science Foundation under GrantNumbers DMS-1659288. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] G. Bingham, B. Yip, M. Ferguson, C. Nansalo, C. Chen, Y. Wang, and T. Kling. MORPH-II:Inconsistencies and Cleaning Whitepaper, 2017. http://libres.uncg.edu/ir/uncw/f/wangy2017-1.pdf.[2] G. Guo and G. Mu. Human age estimation: What is the influence across race and gender? InComputer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer SocietyConference on, pages 71–78. IEEE, 2010.[3] G. Guo and G. Mu. A study of large-scale ethnicity estimation with gender and age variations. InComputer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer SocietyConference on, pages 79–86. IEEE, 2010.[4] G. Guo and G. Mu. Simultaneous dimensionality reduction and human age estimation via kernelpartial least squares regression. In Computer vision and pattern recognition (cvpr), 2011 ieeeconference on, pages 657–664. IEEE, 2011.[5] K. Ricanek and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th InternationalConference on, pages 341–345. IEEE, 2006.8

MORPH-II: A proposed subsetting scheme

http://libres.uncg.edu/ir/uncw/f/wangy2017-2.pdf

MORPH-II: A proposed subsetting scheme

Abstract

Similar works

Full text

Available Versions

The University of North Carolina at Greensboro