CHAPTER 5 AGE GROUP CLASSIFICATION OF FACIAL IMAGES USING RANK BASED EDGE TEXTURE UNIT

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1 CHAPTER 5 AGE GROUP CLASSIFICATION OF FACIAL IMAGES USING RANK BASED EDGE TEXTURE UNIT 5.1 BRIEF OUTLINE Human beings can easily categorize a person s age group from an image of the person s face where as this ability has not been pursued in the computer vision community. To address this problem very important area of research, the present thesis proposes a novel scheme of age classification system using features derived from co-occurrence parameters using Rank based Edge Texture Unit (RETU). The Cooccurrence Matrix (CM) on RETU characterizes the relationship between the neighboring edge values, while preserving local information. The novelty of the proposed RETU is it classifies the age of human into seven categories i.e. in the age groups of 1-10, 11-20, 21-30, 31-40, 41-50, 51-60, and greater than 60. The TU of the proposed RETU ranges from 0 to 17 and thus reduces overall complexity in evaluating features from CM. The co occurrence features extracted from the RETU provide complete facial image information for age classification purpose. The RETU reduces each 3 3 sub image into 2 2 sub image while preserving the texture features and thus reduces the overall dimensionality of the image. 5.2 INTRODUCTION The human face provides the observer with much information on gender, age, health, emotion and so on. Indeed, considerable research on 85

2 the human face has taken place in psychology and in the other cognitive sciences quite early. In recent years, applications in the area of human communication are actively studied from the viewpoint of information technology. A major goal of such studies is to achieve automatic identification of individuals using computers. To incorporate a human-face database in such applications, it is required to solve the issue of age development of the human face. While studying physical changes due to the ageing process many researchers tried to classify facial images into various groups [35, 55, 56, 115]. The authors carried out classification of: babies and adults [102], two age groups and [35], sex [35, 55]. So far, no study has attempted to classify the age groups with a span of ten years, based on RETU-CM features. Due to this reason, the present thesis attempted the tedious and complex task of classification of age into seven groups based on rank based edge texture units (RETU). The age groups are classified in to 1-10, 11-20, 21-30, 31-40, 41-50, and above 60 by deriving a new classification algorithm on the co-occurrence features derived from the novel approach called RETU. Recently, Vijaya kumar V, Chandra mohan et al [ 110 ] classified the age groups into seven categories i.e in the age groups of 16 to 25, 26 to 35, 36 to 45, 46 to 55, 56 to 65, 66 to 75 and 76 to 85 by using Topological Texture Features (TTF) in the facial skin. They [110] observed the fact that the facial skin of a person tends to undergo more changes with growing age. These rapid topological changes in the skin are exploited by TTF s using mathematical morphology. The TTF s are derived from the 86

3 patterns formed by Bezier, Koch and Elliptic curves and U, V, I, T and Z patterns on the facial skin. Wen-Bing Horng, Cheng-Ping Lee and Chun-Wen Chen et.al [124] considered four age groups for classification, including babies, young adults, middleaged adults, and old adults. Their method [124] is divided into three phases: location, feature extraction, and age classification. Based on the symmetry of human faces and the variation of gray levels, the positions of eyes, noses, and mouths are located by applying the Sobel edge operator and region labeling in the above methods [124]. Age classification on facial images based on cranio-facial development theory and skin wrinkle analysis [130], considered only three age-groups babies, young adults, and senior adults. The computations are based on cranio-facial development theory and skin wrinkle analysis. This method [56] is complex in nature and obtained only 81.57% of correct classification. 5.3 PROPOSED METHOD FOR AGE CLASSIFICATION ON FACIAL IMAGES BASED ON RETU-CM The proposed RETU-CM consists of seven steps. The block diagram of the proposed method is shown in figure 5.1. Original color image Cropping Cropped image Conversion of color to gray Robinson compass masks Extract edges from cropped face image RETU matrix Co-occurrence Matrix Extract features Classification Figure 5.1: block diagram of RETU-CM for age group classification system. 87

4 The original facial image is cropped based on the location two eyes of in the first step as shown in figure 4.5. In the step 2, if the facial image is a color image then it is converted into a gray level facial image by using HSV color model. In the third step, edge image is obtained by converting a 3 3 sub image into a 2 2 sub image by using Robinson compass masks. In the fourth step, rank values are assigned on the obtained edge image. In the fifth step, RETU are evaluated. In the sixth step co-occurrence matrix (CM) is formed on RETU. In the seventh step, statistical features are evaluated on the new RETU-CM for age group classification. In the last step a new algorithm is derived for an efficient age classification system based on the feature set derived from the proposed RETU-CM features RGB to HSV color model conversion In color image processing, there are various color models in use today. In order to extract gray level features from color information, the proposed RETU-CM utilized the HSV color space. In the RGB model, images are represented by three components, one for each primary color red, green and blue. Hue is a color attribute and represents a dominant color. Saturation is an expression of the relative purity or the degree to which a pure color is diluted by white light. HSV color space is a nonlinear transform from RGB color space that can describe perceptual color relationship more accurately than RGB color space. The present chapter has used HSV color space model conversion, because the present study is aimed to classify the human age in to seven groups with a gap of 10 years. HSV color space is formed by hue (H), saturation (S) and value (V). Hue denotes the property of color such as blue, green, red. Saturation 88

5 denotes the perceived intensity of a specific color. Value denotes brightness perception of a specific color. However, HSV color space separates the color into hue, saturation, and value which means observation of color variation can be individually discriminated. In order to transform RGB color space to HSV color space, the transformation is described as follows: The transformation equations for RGB to HSV color model conversion is given below. =max,, (5.1) S =,, (5.2) H= V=R (5.3) H= + V=G (5.4) H= + V=B (5.5) where the range of color component Hue (H) is [0,255], the component saturation (S) range is [0,1] and the Value (V) range is [0,255]. In this work, the color component Hue (H) is considered as color information for the classification of facial images. Color is an important attribute for image processing applications Extraction of edge information from the facial image The proposed RETU scheme generates the edges in the second stage. Various low-level visual features (e.g. color, texture, shape, edges) can be extracted as a preprocessing step from the images. Image edges give good information about the image content because they allow the identification 89

6 of the object structures. Edge detection is a fundamental tool used in most of the image processing applications to obtain information from the images as a precursor step to feature extraction. The edges contain the following features of the image. 1. Edge aims at identifying points in a digital image at which the image brightness changes sharply or more formally and has discontinuities. 2. Edge detection process detects and outlines the boundaries between objects and the background in the image. 3. Edge features are useful to overcome the problems generated by noise, edge strips and acuity. 4. Edges form boundaries between the different textures. 5. Edge reveals the discontinuities in image intensity from one pixel to another. Based on the above, the present chapter found that edges are relatively a good choice for obtaining facial image attributes or contents. The proposed RETU uses local edge features as the preprocessing step. The proposed method evaluates edge features on the entire cropped facial image. For evaluating the edges the Robinson Compass edge operator is used. The Robinson Compass edge operator provides better edge information with an advantage of being less sensitive to noise and extract explicit information about edges in any direction. Any edge detection masks can be extended by rotating them like compass masks, which will allow to extract explicit information about 90

7 edges in any direction. Robinson Compass edge detection is applied on the facial image to segment the enhanced borders from the background image. The Robinson edge detector applies Robinson approximation to the derivative of the facial image and detects edges in facial image. The masks are defined by taking a single mask and rotating it to eight major compass orientations: North, Northwest, West, Southwest, South, Southeast, East and Northeast as represented in figure 5.2. These masks are symmetrical about their directional axis, which contains zeros. The present study applied only the four primary Robinson compass masks { r 0, r 1, r 2 and r 3} on each 3 3 neighborhood of facial image by taking the absolute differences. The remaining four masks are not considered because they are just reflections of the above four. By applying r 0, r 1, r 2 and r 3 masks on the sub image of 3 3 mask four grey values which are nothing but edge values i.e. E 0(r 0), E 1(r 1), E 2(r 3) and E 3(r 3) are obtained. This leads to the formation of a 2 2 sub image from 3 3 sub image as shown in figure 5.3. Figure 5.2: Robinson compass masks or edge detection masks P 1 P 2 P 3 P 4 P 5 P 6 E 0(r 0) E 1(r 1) P 7 P 8 P 9 E 2(r 2) E 3(r 3) (a) (b) Figure 5.3: (a) A sub image of 3 3 (b) obtained 2 2 edge matrix 91

8 5.3.3 Generation of RETU from Edge matrix: The proposed method extracted local image information in the form of texture unit from obtained 2 2 edge matrix as represented in figure 5.4. The obtained 2 2 edge matrix represents the smallest complete unit and it is different from usual texture unit that is represented in the literature. In the proposed method a 2 2 sub edge image is decomposed into a set of essential small unit called texture unit (TU), which characterize the local texture edge information. The following procedure is used to derive RETU of the TU values. For each 2 2 edge representation the following steps are evaluated to obtain RETU. 1. Sort the elements of each 2 2 edge matrix. 2. Assign the ranks (0, 1, 2 or 3) to the sorted elements of edge matrix (E 0(r 0), E 1(r 1), E 2(r 3) and E 3(r 3)). While assigning the rank values, if any values in 2 2 edge matrix are same then same rank is assigned. 3. By step two each element of TU (R 1, R 2, R 3 and R 4), will have only one of the four possible values {0, 1, 2 or 3}. From this TU value is calculated by using the equation 5.6. This texture unit is named as Rank based Edge Texture Unit (RETU). 4, 1 /2 =1,2,3,4 5.6 where x i is the element of TU in 2 2 edge matrix. By equation 5.6 the TU values of the proposed RETU ranges from 0 to 17 only. There is no unique way to label and order the 17 texture units. 92

9 The illustration process of generating the RETU from original facial image is shown in figure 5.4. The proposed RETU reduces the original matrix of size N M into N/3 M/ (a) (b) ( c) (d) Figure 5.4: The illustration process of RETU (a) original image (b) edge image after applying Robinson compass masks (c) rank values on edge image (d) RETU Generation of Co-occurrence matrices on RETU matrix Grey level co-occurrence matrices (GLCM) introduced by Haralick [33] attempt to describe texture by statistically sampling how certain grey 93

10 levels occur in relation to other grey levels. One of the major inconveniences of Co-occurrence Matrix (CM) is the large range of its possible values (256 gray values) at the same time that these values are not correlated. It also requires more computation time. In general, the size of CM depends on gray level range of the image. To reduce gray values on images and also to reduce overall dimension of the image, the present study used CM on the proposed RETU. A set of RETU-CM, features defined by Haralick are extracted on facial image. The features used in this method are energy, entropy, inertia, local homogeneity, correlation, and cluster shade. They are represented from equations ( ). The proposed RETU-CM combines the merits of both statistical and structural information of images and thus represents complete information of the facial image. Entropy= ln P P, Energy= ln P, Inertia= P i j, P Local Homogenity= 1+ i j, Correlation= P, i µ j µ σ 5.11 h = i M + j M P,

11 5.4 RESULTS AND DISCUSSIONS The proposed scheme established a database of the 1002 face images collected from FG-NET database, 500 face images collected from Google database and other 600 images collected from the scanned photographs. This leads a total of 2102 sample facial images. Sample images of each group images are shown in figure 1.4. The present proposed RETU-CM classified the seven different age groups by using the following two methods. 1) Proposed age group classification algorithm on RETU-CM features. 2) Using Leave one out method on RETU-CM features Age group classification by the proposed algorithm on the proposed RETU-CM features. The proposed method classifies the facial images into seven age groups 1 to 10, 11 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60 and greater than 60. The statistical features are extracted from RETU-CM of different age groups of facial images and the results are stored in the feature database. Feature set leads to representation of the training images. The statistical features of seven groups of facial images are shown in tables 5.1, 5.2, 5.3, 5.4, 5.5, 5.6 and 5.7 respectively. The classification algorithm to classify the facial image is given in algorithm 5.1. To show the significance of the proposed RETU-CM method, probe or test images are taken. On probe image, RETU-CM features are evaluated on the facial image. Based on the age group classification algorithm using RETU-CM features, the probe images are tested. For this 40 probe images are collected randomly from various data bases, belonging to various age groups are given in table 5.8. The table 5.8 gives the classification rate of 92.5%. 95

12 Table 5.1: RETU-CM feature set values of age group from 1 to 10 years SN IMAGE Homo correlation shade Cluster Inertia Energy Entropy O NAME geneity 1 001A A A A A A A A A A A A A A07a A07b Table 5.2: RETU-CM feature set values of age group from 11 to 20 years SNO IMAGE Homogeneitrelation shade Cor- Cluster Inertia Energy Entropy NAME A A A A A A A A A A A A A16a A16b A

13 Table 5.3: RETU-CM feature set values of age group from 21 to 30 years SNO IMAGE Homo- Cor- Cluster Inertia Energy Entropy NAME geneity relation shade A A A A A A A A A A A A A A A22a Table 5.4: RETU-CM feature set values of age group from 31 to 40 years SNO IMAGE Homo Cor Cluster Inertia Energy Entropy NAME geneity relation shade A A A A A A A A A A A A A A A

14 Table 5.5: RETU-CM feature set values of age group from 41 to 50 years IMAGE Homo Cor- Cluster SNO Inertia Energy Entropy NAME geneity relation shade A43a A A A A A A A A A A A A A43b A Table 5.6: RETU-CM feature set values of age group from 51 to 60 years SN IMAGE Homogeneity relation Cor- Cluster Inertia Energy Entropy O NAME shade 1 006A A A A A A A A A A

15 Table 5.7: RETU-CM feature set values of age group greater than 60 years SN IMAGE Homogeneity relation Cor- Cluster Inertia Energy Entropy O NAME shade 1 006A A A A A A A A A A A Algorithm 5.1 : Age group classification using RETU-CM features if ( ENTROPHY < 100 ) print ( facial image age is 1-10) Table. 9: classification rates of facial databases else if ( ENERGY < 1000) % correct classification print ( Image facial image Database age is ) rates FG-NET ageing else if ( HOMOGENITY database > 200 ) print Google ( facial Images image age is ) Scanned Images else if ( CORRELATION < 0.02 ) print ( facial image age is > 60 ) else if ( CLUSTER SHADE < 9000 ) print ( facial image age is 31-40) else if ( INERTIA > 150 ) print ( facial image age is 41-50) else print ( facial image age is 51-60) end 99

16 Table 5.8: successful classification results of test data bases for the Image name Inert ia proposed RETU-CM method. Entrop y Homo geneit y Energy correlatio n Cluste r shade Classifi ed age group Result 002A Success 002A Success 014A Success 015A Success 014A Success 021A Success 021A Fail 021A Success 025A Success 025A Success 027A Success 028A Success 028A Success 028A Success 033A Success 039A Success 047A Success 048A Success Sci Success Sci Success Sci >60 Fail Sci >60 Success Sci >60 Success Sci Success Success Success Success Success Success Success Success Success Success 100

17 Success Success Fail Success Success Success Success Age group classification by Leave one out method: The present chapter used another classification technique called Leaves One Out classifier [51] for the age group classification. To evaluate the correct classification on the proposed RETU-CM using the above classifier the following three situations are considered: Setup 1: The training set considers only the original sub images and the others are used for testing. (Approximately 20% are used for training and 80% for testing) Setup 2: The training set considers of sub images with rotation angle of 0 0, 45 0, 90 0, and, (approximately 30% are used for training and 70% for testing). Setup 3: The training set considers half of all sub images that are selected randomly (50% are used for training and 50% for testing). The absolute difference of the feature vector values of the query image and database images are calculated. After that, a threshold and k- NN classifier is used to measure the similarity between query image and the database images. The classification results of different age groups are tabulated in table 5.9 using leave one out method, where each entry corresponds to the 101

18 average correct classification rate of different kinds of facial images. By comparing the table 5.9 and 5.10 it is clearly evident that the classification by Leave One Out method, and the proposed age classification algorithm on the proposed RETU-CM classifies the facial image into seven groups with an average classification rate of 93%. and 95% respectively. The complexity of the Leave One O0ut method is high when compared to the proposed classification algorithm. So the present chapter observed from table 5.9 the classification rate for seven age groups by using leave one out method on the proposed RETU-CM features energy, entropy and contrast gives high classification rate compared to remaining features. Table 5.9: Mean % classification of facial images Group Inertia Energy Entropy Homo geneity correlation Cluster shade > Comparison with other Methods Though the proposed classification algorithm based on RETU-CM is powerful in classification of age groups with a span of 10 years. Still it is compared with various existing algorithms. In skin wrinkle geograph map [124] three age groups are classified i.e. child, adults and senior adults between age groups 1 to 10, and 102

19 above 60 respectively. Since 7 age groups are not classified by [124], the proposed age classification method on RETU-CM features is modified to classify age groups into three groups in the following way by using only entropy and homogeneity parameters. The proposed algorithm is given below. The classification results by the proposed method and skin wrinkle analysis are given in table Table 5.10: % mean classification rates for proposed method and craniofacial development theory and skin wrinkle analysis method. Image cranio-facial development Proposed Method theory and skin wrinkle RETU-CM analysis Child age 78.15% 97.23% adults 79.31% 96.42% Senior adults 81.57% 97.57% Algorithm 5.2: Age group classification to classify age into three categories by the proposed RETU-CM features if ( ENTROPHY < 100 ) print ( facial image age is child s age) else if ( HOMOGENITY < 13) else end print ( facial image age is adult s age) print ( facial image age is senior adult s age) In another Facial Feature method [130] four age groups are classified as babies, young-adults, middle age adults, old adults between the age group 1 to 2, 3 to 39, 40 to 60 and above 61years respectively. The 103

20 proposed age classification algorithm based on RETU-CM used only clustershade and energy parameters to classify above age groups. The proposed algorithm is given below and the classification results are given in table Table 5.11: % mean classification rates for proposed method and Facial Image feature method Facial Features Proposed Method RETU-CM Babies 92.52% 100% Young Adults 91.72% 96.37% Middle age adults 91.47% 97.23% Senior adults 90.07% 96.78% Algorithm 5.3: Age group classification to classify age into four categories by the proposed RETU-CM features if (CLUSTER SHADE < 10 ) print ( facial image age is Baby) else if ( CLUSTER SHADE > 100) AND CLUSTER SHADE > 10000) print ( facial image age is YOUNG adult s age) else if ( ENERGY < 2000) else end print ( facial image age is MIDDLE adult s age) print ( facial image age is OLD adult s age) Various authors classify the age groups into various categories. The present study tabulated the various age classification rates in the following table

21 Table 5.12: Comparison with Other Methods S. No. Authors Name of the Method % of Classifica -tion Rate Type of Age Classification Proposed Method (RETU- CM) Chandra Sekhar et al.,[12] Chandra Mohan et al.[11] Chandra Mohan et al.[13], Chandra Mohan et al., Chandra Mohan et al[14]., Young H. Kwon, et al.[130] Tsuneo KANNO, et al., Wen-Bing Horng, Cheng-et al.[124], Age group classification of facial images using rank based edge texture unit Texton based shape features on local binary pattern for age classification Novel Method for Child and Adulthood Classification (CAC) Based on Geometrical Features of Facial Image. Novel Method of Child and Adulthood Classification Using Linear Wavelet Transforms. Age Classification of Adults Based on Topological Texture Features of Facial Skin. Novel Method of Adult Age Classification Using (2-level) Linear Wavelet Transforms. Age Classification from Facial Images, Computer Vision and Image understanding Vol. 74, No. 1, April, pp. 1 21, Classification of Age Group Based on Facial Images of Young Males by Using Neural Networks, IEICE Trans.. Inf. & Syst., Vol.E84 D, No.8, August Classification of Age Groups Based on Facial Features, Tamkang Journal of Science and Engineering, Vol. 4, No. 3, pp (2001) % , 11-20,21-30, 31-40, 41-50, and > 60 Child Adulthood Child Adulthood Child Adulthood and and and 16-25, 26-35, 36-45, 46-55, 56-65, and , 26-35, 36-45, 46-55, 56-65, and Babies, adults, and Senior adults. Only young males are age groups considered for classifications are 12, 15, 18 and 22 years. Classified age groups are babies, young adults, middleaged adults, and old adults SUMMARY The proposed method RETU-CM features classifies the facial images into seven age groups (1-10, 11-20, 21-30, 31-40, 41-50, 51-60, and age 105

22 greater than 60 years) very efficiently and effectively. So far, no research has attempted, for this type of age classification. The proposed method derived a new age group classification feature using rank based edge texture units. The novelty of the proposed method is, that reduces the overall dimension of the image by three times while preserving the local features. The other advantage of the derived RETU features is it reduced the TU size from 0 to 6561 and 0 to 79 to 0 to 17 as in the case of original TU and fuzzy texture units respectively. That is why the proposed RETU is more suitable for the evaluation of Co-occurrence features and it reduced greatly the overall complexity. The proposed method does not need a particular classification algorithm. The complexity of proposed method is reduced drastically because of its simplicity than Leave One Out method. The proposed method RETU-CM features is also tested by using leave one out classification method for classification of age groups into seven groups (1-10, 11-20, 21-30, 31-40, 41-50, 51-60, and age greater than 60 years). This technique also yielded a classification rate of above 95%. When compared with other approaches, the proposed scheme is more effective and exhibiting effective and significant classification ability by using minimum subset of co-occurrence features (i.e., energy, entropy and contrast). The experimental results clearly indicate the efficacy of the proposed RETU-CM over the various the other existing methods. There are several directions that need to be further explored. The problem of varying orientation of the face needs to be addressed. 106