Utilizing Posterior Probability for Race-composite Age Estimation

Transcription

1 Utilizing Posterior Probability for Race-composite Age Estimation Early Applications to MORPH-II Benjamin Yip NSF-REU in Statistical Data Mining and Machine Learning for Computer Vision and Pattern Recognition The University of North Carolina at Wilmington

2 Presentation Outline 1. Introduction 2. MORPH-II 3. Image Pre-processing 4. Methods 5. Preliminary Results 6. Conclusions 1

3 Introduction

4 The Importance of Age Estimation The human face encodes a wealth of information about the individual; when properly analyzed, this information becomes valuable data that can be used in a wide array of applications [2]: Electronic Customer Relationship Management (ECRM) 2

5 The Importance of Age Estimation The human face encodes a wealth of information about the individual; when properly analyzed, this information becomes valuable data that can be used in a wide array of applications [2]: Electronic Customer Relationship Management (ECRM) Surveillance monitoring 2

6 The Importance of Age Estimation The human face encodes a wealth of information about the individual; when properly analyzed, this information becomes valuable data that can be used in a wide array of applications [2]: Electronic Customer Relationship Management (ECRM) Surveillance monitoring Biometrics 2

7 Previous Research Previous research [3] has shown that age estimation is highly sensitive to race and gender categories. However, as far as we aware, no previous models have taken multiracial individuals into account. 3

8 Project Overview MORPH-II Preprocessing Gender Clsf. Female Race Classification Male Race Classification BF Age WF Age BM Age WM Age Estimator Estimator Estimator Estimator Figure 1: The four subgroup age estimators correspond to: black females (BF), white females (WF), black males (BM) and white males (WM) 4

9 Utilizing Posterior Probability Let b, w [0, 1] be the posterior probabilities of belonging to each race, where b + w = 1. Race Classification b w Black Age Estimator White Age Estimator Ŷ B Ŷ W Y = (b Ŷ B ) + (w Ŷ W ) 5

10 MORPH-II

11 Introduction to MORPH-II Table 1: Number of Images by Gender and Race Black White Asian Hispanic Other Total Male 36,821 7, , ,644 Female 5,756 2, ,490 Total 42,577 10, , ,134 Table 2: Number of Distinct Individuals Black White Asian Hispanic Other Total Male Female Total

12 Introduction to MORPH-II Images per Individual Age Distribution Number of Individuals images Frequency Number of Images Age Figure 2: Images per Subject Figure 3: Age Distribution 7

13 Image Pre-processing

14 Objectives of Pre-processing Pre-processing is a very important operation in computer vision tasks, as valuable information is usually enveloped in meaningless space. Accordingly, the absence of a pre-processing step can have drastic consequences on the results. 8

15 Pre-processing Objectives Given a standard mugshot, the objective is to: yielding the pre-processed face. 1. ensure proper rotation, 2. extract only the face, and 3. standardize the image, 9

16 Pre-processing MORPH-II Figure 4: Stages of Pre-processing Figure 5: Challenging Images 10

17 Methods

18 Subsetting Given the heavily imbalanced nature of MORPH-II, only images of Black and White individuals were used in this project. These images were subsetted according to the below scheme: bf_1 wf_1 bm_1 wm_1 f1 m1 bf_2 wf_2 bm_2 wm_2 f2 m2 Figure 6: Subsetting Scheme In this reduced dataset there are a total of 20,560 images in a 3 to 1 Male to Female ratio. The images for each gender are split into two halves, and every race and gender subgroup is divided similarly (±1 image). There are no common individuals between subsets. 11

19 Feature Extraction After undergoing histogram equalization to correct for illumination variation, features were extracted from each image using Local Binary Patterns (LBPs); these feature vectors were used for all stages of the age estimation pipeline. Figure 7: Examples of LBPs 1 A Note on Dimensionality Reduction The dimension of the LBP feature vectors was reduced using Principal Component Analysis (PCA). 400 principal components were kept

20 Race Classification Race Classifier Support Vector Machine (SVM) with a linear kernel Used to obtain posterior probabilities Table 3: Race Classification Accuracies train : test Accuracy Cost f1 : f f2 : f m1 : m m2 : m Posterior probabilities are obtained by fitting a sigmoid/logistic model to the SVM outputs (Platt scaling) [4]: P(y = 1 f) = exp(af + B) (1) 13

21 Age Estimation Race Classification b w Model Support Vector Regression (SVR) with a linear kernel Black Age Estimator Ŷ B White Age Estimator Ŷ W Y = (b Ŷ B ) + (w Ŷ W ) 14

22 Preliminary Results

23 Partial Dataset The Partial (even) dataset contains 1,000 images of 1,000 distinct individuals; it differs from the reduced version of MORPH-II in that the overall age distribution is uniform. Table 4: Results on Partial Dataset MAE Weighted MAE bf_ bf_ wf_ wf_ bm_ bm_ wm_ wm_

24 Full Dataset (reduced) Table 5: Results on Full Dataset (reduced) MAE Weighted MAE bf_ bf_ wf_ wf_ bm_ bm_ wm_ wm_

25 Conclusions

26 Conclusions While the results from the Partial dataset seem to show that the composite age estimate is an improvement over the straightforward prediction, the preliminary results from the reduced MORPH-II dataset are mixed. Much more work remains to be done in testing the model on the full dataset. 17

27 Future Work The race labels do not account for multiracial individuals; therefore, there may be mixed race individuals in the training sets that are diluting the models. To correct for this: 1. Assemble training sets of individuals who are not of mixed race (i.e. they fall clearly into the Black or White race categories) 18

28 Future Work The race labels do not account for multiracial individuals; therefore, there may be mixed race individuals in the training sets that are diluting the models. To correct for this: 1. Assemble training sets of individuals who are not of mixed race (i.e. they fall clearly into the Black or White race categories) 2. Use these sets to train race classifiers and age models 18

29 References i G. Bingham, B. Yip, M. Ferguson, and C. Nansalo. MORPH-II: Inconsistencies and Cleaning. Y. Fu, G. Guo, and T. S. Huang. Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11): , Nov G. Guo and G. Mu. Human age estimation: What is the influence across race and gender? In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pages 71 78, June 2010.

30 References ii J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages MIT Press, K. Ricanek and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In Automatic Face and Gesture Recognition, FGR th International Conference on, pages IEEE, 2006.