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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...
In this paper, we propose a new divide-and-conquer based method, called fusion of multiple binary age-grouping-estimation systems, for human facial age estimation. Under a specific constraint, such as a given facial feature or classification/regression method, what is the better framework for age estimation? First we employ multiple binary-grouping systems for age group classification. Each face image...
This paper introduces a novel age estimation method using a new texture descriptor Weber Local Descriptor (WLD). This texture descriptor is analyzed in depth for age estimation problem. In the study, the multi-scale versions of holistic and spatial WLD (SWLD) descriptors are used to extract the age related features from normalized facial images. After finding a lower dimensional feature subspace,...
Existing age estimation algorithms based on facial images have been showing high dependency on the age range with the range 29–49 yielding the best estimation results. This paper introduces a new multi-stage binary age estimation (MSAE) system configured as a network of decision making neural network (NN) and support vector machine (SVM) units. The decision making process was based on the classification...
Estimating the age of a human from the captured images of his/her face is a challenging problem. In general, the existing approaches to this problem use appearance features only. In this paper, we show that in addition to appearance information, facial dynamics can be leveraged in age estimation. We propose a method to extract and use dynamic features for age estimation, using a person’s smile. Our...
Estimating the date of undated medieval manuscripts by evaluating the script they contain, using document image analysis, is helpful for scholars of various disciplines studying the Middle Ages. However, there are, as yet, no systems to automatically and effectively infer the age of historical scripts using machine learning methods. To build a system to date medieval documents is a challenging problem...
Many age estimation methods have been proposed for various applications such as Age Specific Human Computer Interaction (ASHCI) system, age simulation system and so on. Because the performance of the age estimation is greatly affected by the aging feature, the aging feature extraction from facial images is very important. The aging features used in previous works can be divided into global and local...
Human age estimation using facial image is becoming more and more investigated because of its potential applications in many areas such as multimedia communication and human computer interaction. Since many factors contribute to the aging process like gender, race, health, living style, the current age estimation performance for computer vision systems is still not efficient enough for practical use...
Over the recent years, a great deal of effort has been made to age estimation from face images. It has been reported that age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions. However, it is not straightforward to achieve the same accuracy level in real-world environment because of considerable variations in camera settings,...
Face age estimation is a difficult problem due to the dynamics of facial aging and its complex interactions owing to genetics and behavior factors. In this work we develop a robust age estimation system tuned by model selection that outperforms all prior systems on the FG-NET face database. We study various model selection methods systematically to determine the best selection methods among Least...
Due to the temporal property of age progression, face images with agingingg features display some sequential patterns with low-dimensional distributions, which can be effectively extracted by subspace learning algorithms. The patterns extracted by traditional subspace learning methods are mostly restricted to a certain database. As a result, the performance cannot be generalized when applying these...
This paper proposes a new age estimation method by using Active Appearance Model(AAM) to extract the regions of age features. This method consists of four modules: detecting face, searching facial feature regions, finding age features, and age estimation. In the experiments, we demonstrate that the extracted age features can be applied to estimate age effectively. Using the portrait images of 200...
In this paper, we introduce a novel age estimation technique that combines Active Appearance Models (AAMs) and Support Vector Machines (SVMs), to dramatically improve the accuracy of age estimation over the current state-of-the-art techniques. In this method, characteristics of the input images, face image, are interpreted as feature vectors by AAMs, which are used to discriminate between childhood...
Estimating the age exactly and then producing the younger and older images of the person is important in security systems design. In this paper local binary patterns are used to classify the age from facial images. The local binary patterns (LBP) are fundamental properties of local image texture and the occurrence histogram of these patterns is an effective texture feature for face description. In...
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