Age Estimation using Local Matched Filter Binary Pattern

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1 Age Estimation using Local Matched Filter Binary Pattern Imad Mohamed Ouloul,KarimAfdel, Abdellah Amghar Laboratory MTI University Ibnou Zohr B.P 8106, Agadir, Morocco Laboratory SIV University Ibnou Zohr B.P 8106, Agadir, Morocco Abstract The automatic age estimation systems from facial images are often very complex and difficult to achieve, because of the aging process complexity. Such a system can be used in security, man-machine interactions, and biometrics. Research in this field has advanced during the last years. This paper proposes an age estimation system based on the shape and gray level texture intensity, extracted from facial images. The main contribution of this work is the design of a new descriptor named Local Matched Filter Binary Pattern, which detects and encodes face areas containing wrinkles. This descriptor, combined with parameters extracted by the active appearance model, enables the design of a high discriminative age. The experiments performed on the FG-net database, and the compared results approved this descriptor s efficiency in age estimation. Keywords Age Estimation, Local Matched Filter binary pattern, Active Appearance Model I. INTRODUCTION The automatic age estimation systems from facial images recently attracted the attention of researchers in image processing, specialized in computer vision. This system has many applications, which are related to biometrics, demographic analysis, security, vending machines control, surveillance, access control to adult websites, etc. However, the development of such a system has two major problems. First, there is a difficulty in predicting the aging process due to the large number of the involved factors (race, gender, health, gene, climate, social status, etc). The face contains two types of features which serve in the age estimation task : the shape, which represents the geometric configuration of facial landmarks; and the texture (flat area, wrinkles, and age spots), which reflects the state of the facial skin. The changes that each facial feature experiences are different. The shape variation is limited to a period from birth to 21 years old, and the texture variations start from 25 years old onward. In general, an age estimation system consists of two steps, the facial features extractions and age prediction. In the first step, the age discriminative features are extracted using the texture descriptors LBP [1], GW [2], LPQ [3], or models of facial representation like anthropocentrism model [4], AAM [5], and aging pattern subspace [6]. In the second step, the prediction can be achieved by three different approaches, classification, regression, or hierarchical [7]. This latter approach (combining both classification and regression) divides the images into several age groups with a distinct regression for each group [8]. In [9] A. latins et al has experimentally proven that the hierarchical approach is the most efficient for age estimation. Texture descriptors have shown their performance in the field of facial recognition and detection. However, they are far from being efficient on their own for age estimation, because these descriptors do not take shape features under consideration. On the other hand, facial representation models combine both the shape and global texture; but the major issue with this model is the smoothening of facial wrinkles. To overcome this problem, a combination of a texture descriptor and a facial representation model is achieved to build a high discriminative age [2], [3]. This paper presents a new descriptor efficient for facial wrinkles, named Local Matched Filter Binary Pattern (LMFBP). This descriptor enables both the detection and extraction of wrinkles. This matched filter handles the detection task, while the local binary pattern enables wrinkle extraction and presents them in a histogram, which is used to train the classifier. The matched filter is proposed in the signal processing to detect signals whose form is predetermined. It was used for the first time by O Gorman et al in image processing for fingerprint detection [10], and then by Chaudhuri et al in retinal images detection [11]. Furthermore, LBP [12] is one of the most frequently used texture descriptor. It enables texture encoding by finding the difference between a given pixel and its neighboring pixel. The rest of this paper is organized as follows : a brief presentation of the most significant works in section 2, a description of LMFBP based age estimation system is achieved in section 3, a presentation of the experimental results and analysis in section 4, and a conclusion in section 5. II. RELATED WORK The first published work relating to age estimation was proposed by Kown et al in [4]. The basic idea of this work is limited to estimate age classes (child, young, senior, adult) of each face from a comparison of facial anthropometric measures. Kown et al s work is far from being applied to the real world due to the extraction difficulty of anthropometric measures from images taken in non-controlled environments (face pose change). In [13], latins et al have proposed an aging function able of age estimation using a feature combination of the active appearance model. Furthermore, Lantis et al have /16/$ IEEE

2 achieved a classifier comparison for age estimation in [9]. In this comparison, the hierarchical approach provided the best performance. In [6] Geng et al proposed a method named AGing pattern Subspace (AGES). The main idea of this method is to build a representative Subspace. First, PCA is performed to a set of facial images of a single individual (sorted in ascending age order), then the age estimation is achieved by projecting facial images in the Subspace (preconstructed in the learning phase). An extended AGES version in proposed in[14]. The major difference between the two versions is the AAM usage to build a representative Subspace instead of the PCA. In [1], LBP is used for encoding the facial image s texture into a histogram. The extended LBP version is used [13] for extracting uniform patterns and/or invariant rotation patterns. Txia et al in [5] used predefined patches in facial images to extract a high discriminative robust to facial appearance variations between men and women. In general the image patches extraction is based on facial landmarks detection by AAM or ASM. III. PROPOSED APPROACH FOR AGE ESTIMATION The age estimation system that we are introducing consists of two phases. An overview of our approach is shown in figure 1. In the first phase, we use AAM to encode the shape and gray level intensity of the facial image. To overcome the problem of smoothening local details of texture, we also use Local Matched Filter Binary Pattern to detect local features (wrinkles and sagging skin) from aligned images. After the features extraction, a normalization and dimension reduction is performed. In the second phase, the prediction is performed by a hierarchical approach, which consists of using classification and regression successively. In the prediction phase, the facial features are divided into age groups, then a distinct regression for each group is performed. A description of the main elements will be presented. A. Active appearance model The Active appearance model is proposed by Cootes et al in [15]. This model combines variations of both shape and texture. It is widely used to interpret and describe deformable objects [16], and to achieve face recognition and age estimation. The appearance model is generated as follows : in the learning phase, the manually annotated points in each image (figure 2(a)) are aligned into a common co-coordinate frame; then PCA is performed to the whole set of points to build a model that describes the shape variation. This model allows to estimate the shape of any given image using this equation 1. s = s + P s b s (1) where s is the estimated shape, s is the mean shape of dataset, P s is the eigenvalue of shape variation, and b s is the vector of shape parameters. However, in order to generate a model that describes texture variation, the whole set of learning images is warped using triangulation (figure 2(b)). Then a normalization and gray level intensity estimation in each triangle is performed. Next, PCA is performed to generate Fig. 1. overview of the proposed system for automatic age estimation (a) Fig. 2. sample FG-net image: (a) image with 68 landmarks, (b) image triangulation based on landmarks a model that describes gray level variations of any given image using this equation 2. (b) g =ḡ + P g b g (2) where g is the gray level intensity, ḡ is the mean gray level intensity of learning images, P g is the eigenvalue of texture variation, and b g is the vector of texture parameters. Lastly, PCA performs a vector parameter combination of both shape and texture b = [b s b g ] to generate an appearance model capable of describing shape and texture variations of each image using this equation 3. b = Qc (3) where Q is the eigenvalue variation of b, and c is the appearance parameters matrix, which controls the shape and texture of a given image. B. Facial image alignment The human face can be divided into three regions. The first gathers wrinkle and sagging skin zones, which are always visible. The second regroups facial zones that might contain wrinkles, but are susceptible to be concealed by facial hair (men) or fringe hair (women). Lastly, the third contains zones

3 (a) (b) (c) Fig. 3. Facial image alignment : (a) before alignment, (b) after alignment, (c) Areas extracted for processing that rarely develop wrinkles (mouth, ears, eyebrows...). In order to develop a useful age estimation system for men and women, we adopt a strategy that consists of using only first region zones (figure 3(a)). These wrinkle zones are cropped based on the landmarks positions [17] detected by the active appearance model. However, face pose changes render the cropping task impossible. To overcome this problem, we make an image alignment based on the eyes relative position (figure 3(b), 3(c)). In our case, the alignment is achieved by a rotation estimated in this equation 4. ( ) x2 x θ = ± arctan (4) y 2 y where θ is the rotation angle, p(x i,y i ) i=1,2 is the eyes position in the Cartesian plan, and c(x, y) is the segment s midpoint connecting both eyes. C. Local Matched Filter Binary Pattern In this sub-section we introduce the Local Matched Filter Binary Pattern descriptor, which we proposed for the detection and extraction of facial wrinkles. Our descriptor is based on the combination of the matched filter and the texture LBP descriptor. The matched filter is proposed for the first time in signal processing to detect signals drowning in Gaussian noise, provided that the signal s mathematical function is predetermined. The matched filter s first applications on images was realized in these works [10], [11], for fingerprint detection and retina image. The main idea of the matched filter is to return the maximum response when the desired shape is found. The achieved observations on a set of facial images (figure 4) enabled us to postulate that wrinkles can be decomposed into others of smaller size and shape closer to Gaussian kernel, regardless the wrinkles actual shape and size. For this reason, the facial wrinkles detection can be implemented by the use of a Gaussian kernel (equation 5). ( ) x 2 G(x, y) = exp 2s 2 for y <L/2 (5) where L is the filter length following the direction y, and s is the parameter that controls the Gaussian kernel s width. In order to detect facial wrinkles in all directions, we generate first a filter bank by a rotation of the Gaussian kernel. We then perform a convolution between the bank and the image to be analyzed. Thereby, the matched filter s response is the local maximum of all the bank s responses (figure 4). On the other hand, the second part of our approach is dedicated to extracting wrinkle containing zones. To perform this task, we were inspired by the works [18], [19], [20], [21] based on Local Binary Pattern[12] for texture description. Therefore, the main idea that we adopted consists of encoding the matched filter s response using Local Binary Pattern. This descriptor is widely used in literature, due to its efficiency for texture description and its simple implementation. D. Age prediction The automatic age estimation is a difficult challenge [6], because of the important number of variables put into play in the aging process. Furthermore, this process can vary considerably between different age phases. In the first and second age phases, changes are generally in the geometric shape. Therefore, the first signs of texture change begin at the second phase s end [2], [23]. To achieve an efficient age estimation system, we adopt the hierarchical approach for prediction. This approach had proven its efficiency in various other works [9]. The hierarchical approach s practical implementation is achieved in two stages. First, the features extracted from facial images are trained to build a classifier that describes age classes using radial kernel SVM. In addition, each group s features are trained anew to build a regressor able of age estimation using linear kernel SVR. The major advantage of this approach is in the reduction of the estimation s margin error, by limiting age phases in each group. IV. EXPERIMENTAL RESULTS A. Database In order to evaluate our system s performance, we use FGnet database. This database is publicly available for academic use. The first release of the FG-net was in From that time, FG-net became the standard for evaluation in this field. This database contains 1002 images divided on 82 subject. In each image, 68 landmarks, which describe the face s geometric shape, are manually annotated. Age distribution in this database ranges from 0 to 69 years old [24]. The figure 5 shows examples of images in ascending age order. From a practical standpoint, we use two metrics to measure the system s performance. The first is the MAE (equation 6) which represents the Mean Absolute Error between the ground truth and the estimated age, and the second is the Cumulative Score (equation 7) which defines the percentage of images whose mean absolute error is low than or equal to a given threshold. MAE = 1 n t i=1 ˆt i=1 (6) N i=1 where ˆt is the estimated age, t is the ground truth age, and N the image number CS(t) = N age<t N 100 (7)

4 Fig. 4. the first line contains images with wrinkles [22]. the second line represents wrinkle detection results by Matched Filter TABLE I RESULTS BEFORE AAM AND LMFBP COMBINATION Acc. Classification (%) MAE AAM LMFBP Fig. 5. sample images from fg-net dataset classified in ascending age order where N age<t is the image number whose estimated age error is low than or equal to a given threshold. B. Results and Analysis In this section, we present the results of the experiments performed on the FG-net database. We used 75% of this database in the learning phase, and 25% in testing. In the performed experiments, we adopted a 5-fold cross validation to optimize the classification and regression parameters respectively for SVM and SVR. In order to avoid the intraclass similarities between images, we followed the Leave- One-Person-Out (LOPO) protocol which consists of using all images of one individual either in the learning phase or in testing [24]. Table I represents the obtained results before the combination of the active appearance model and the local matched filter binary pattern. This first experience shows us that the classification accuracy and the MAE, obtained by the active appearance model, outperforms the LMFBP s results. These results are justified by the parameter s features extracted by AAM, which combine both shape and texture variations. This makes the system more apt to differentiate between age phases (child, young, senior, adult), contrary to the LMFBP s generated parameters which describe only texture variation from local view (wrinkles). In the second experimental phase, we combined the AAM and LMFBP parameters. We used PCA to reduce the descriptor s consequent size. Figure 6 shows the classification precision s variations by SVM depending on the descriptor s size. Figure 7 reflects our system s MAE. These curves analysis shows us that the outperformance state of our estimation system is achieved by the use of 93% of the descriptor s variation. Another important point is that the finale size of the descriptor, used by the system in age estimation, is of low dimension; hence limiting the system resources consumption. Furthermore, the cumulative score, presented in figure 8, shows that the system was able to make an age estimation whose error is low than or equal to 5 Fig. 6. the curve shows age group classification results by radial kernel SVM TABLE II COMPARISON OF OUR METHOD S RESULTS TO OTHER PREVIOUS WORKS Methods of Age estimation MAE MIR [24] 9.49 AGES [14] 6.77 SBAE [8] 6.20 RUL[24] 5.78 LD [24] 5.77 LMFBP 5.09 FO (LOPO) [3] 4.78 years old for 70% of images in testing. Another comparative study, represented in table II, proves that our developed system outperforms many other approaches present in the literature. V. CONCLUSION AND PERSPECTIVE In this paper we developed an automatic age estimation system based on LMFBP. The performed experiments on the FG-net database, along with the comparisons, validated our approach s utility in age estimation. In addition, the

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