A Framework for Age Estimation Using Facial Features

The biometric information contained in a face image is affected by many factors such as living environment, racial differences, and genetic diversity, this complexity leads to the nonstationary of the age estimation. In order to reduce the overlap of face features between adjacent ages and improve the accuracy of age prediction, a multi-stage feature constraints learning method is proposed for face age estimation. The proposed method gradually refines the feature through three feature constraint stages. In each stage, the algorithm continuously updates the feature center of its corresponding age range, and minimizes the distance between each age feature and feature center of the corresponding age range through feature constraint. Feature constraint makes the feature distances between different individuals in the same age feature space smaller and decrease the overlap areas between adjacent age range feature spaces. Meanwhile, the feature distance of different age range feature space is enlarged. The proposed network efficiently merges the features of three stages and optimizes the mapping of feature maps to an ordered binary comparison space. Experiments show that the proposed method is able to effectively improve the discrimination between different age features, and hence to improve the accuracy of face age estimation. In addition, the proposed algorithm is simple enough to achieve fast face age estimation.

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 will be classified into one of the two groups. Within the two groups, two models are trained to estimate ages for the faces classified into their groups, respectively. We also investigate the effect of different age grouping systems on the performance of age grouping accuracy and age estimation error. In the last stage, we propose a sequentially selection algorithm to fuse some of the binary-grouping systems to get a final age estimation result. Experiments on the MORPH2 database demonstrate our framework for age estimation can achieve satisfying results and outperform other state-of-the-art age estimation approaches.

Facial age estimation has grasped the attention of numerous researchers in recent times. It is a challenging task as a consequence of illumination, pose variations, occlusion, complex background, facial expression, and facial makeup. Estimating the age of an individual with an arbitrary pose is quite a challenging job because most of the age estimation system focuses on the frontal view. In this paper, a novel framework for multi-view age estimation by amalgamating the local and global features is proposed. A novel texture feature, Median Gradient Ternary Pattern is proposed in this paper. The Pseudo Zernike Moment extracts the shape features and the View-based Active Appearance Model constructs an appearance model from the facial images. Further, all three features are combined into a feature vector by executing feature-level fusion. The dimension of the combined feature is reduced using Principal Component Analysis. Multi-class Support Vector Machine is utilized to divide the images into four poses. For each pose, a Support Vector Regression with RBF kernel is applied to train a model for estimating the actual age of an individual. The proposed methodology is performed on two databases, namely, FG-NET and CACD which showcase eminent performance.

Age estimation using facial images is applicable in various fields, such as age-targeted marketing, analysis of demand and preference for goods, skin care, remote medical service, and age statistics, for describing a specific place. However, if a low-resolution camera is used to capture the images, or facial images are obtained from the subjects standing afar, the resolution of the images is degraded. In such a case, information regarding wrinkles and the texture of the face are lost, and features that are crucial for age estimation cannot be obtained. Existing studies on age estimation did not consider the degradation of resolution but used only high-resolution facial images. To overcome this limitation, this paper proposes a deep convolutional neural network (CNN)-based age estimation method that reconstructs low-resolution facial images as high-resolution images using a conditional generative adversarial network (GAN), and then uses the images as inputs. An experiment is conducted using two open databases (PAL and MORPH databases). The results demonstrate that the proposed method achieves higher accuracy in high-resolution reconstruction and age estimation than the state-of-the art methods.

DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children

Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age. Due to the differences including races, genders, and other factors, the performance of a learning method for this task strongly depends on the training data. Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames, which can lead to greatly different results. In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos. We use deep regression forests (DRFs) to estimate the age of facial images, while a multiloss convolutional neural network is also utilized to estimate the head pose. Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement. First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses. The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles, thus demonstrating the effect of head pose on age estimation. Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.

Automatic age estimation relying on human facial images is a key technology of many real-world applications, which is still a challenging task in the computer vision field. There are three cascade modules for facial age estimation: facial aging feature extraction, dimension reduction (or feature selection) and estimation method. Many existing literatures focus on the first or last module while for an age estimation system, it's also important to construct a reasonable framework. Our work focuses on creating an effective framework by selecting methods for these modules reasonably. Firstly, a BIM (bio-inspired model) is employed to extract facial aging features because it can not only capture discriminative local and global features, but also overcome interferences of some 2D deformations to some extent. Then, LDA (linear discriminant analysis) is used for reducing the BIF (bio-inspired features) to lower dimensions and extracting more discriminative information at the same time. Finally, CS-OHRank (cost-sensitive ordinal hyperplane rank), which tackles with sparse data well and reflects the cumulative attributes of aging, is applied as the estimation method. Experimental results on benchmark dataset FG-NET show that our framework combining BIF, LDA and CS-OHRank is competitive among the state of the art, with MAE (mean absolute error) = 4.72 years..

We are researching about an age and gender estimation based on wrinkle texture and color of facial images. A preliminary questionnaire (enquete) examination on how effectively the facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. Wrinkles appearing in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research basing on this enquete. Basing on this preliminary consideration, an image-processing algorithm for wrinkle modeling, was proposed. In addition, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing. An efficient interface for displaying the relationships among keywords and facial images has been introduced with 2D/3D-feature space defined by the indexing.

In this paper, a person-specific facial expression recognition method which is based on Personal Facial Expression Space (PFES) is presented. The multidimensional scaling maps facial images as points in lower dimensions in PFES. It reflects personality of facial expressions as it is based on the peak instant of facial expression images of a specific person. In constructing PFES for a person, his/her whole normalized facial image is considered as a single pattern without block segmentation and differences of 2-D DCT coefficients from neutral facial image of the same person are used as features. Therefore, in the early part of the paper, separation characteristics of facial expressions in the frequency domain are analyzed using a still facial image database which consists of neutral, smile, anger, surprise and sadness facial images for each of 60 Japanese males (300 facial images). Results show that facial expression categories are well separated in the low frequency domain. PFES is constructed using multidimensional scaling by taking these low frequency domain of differences of 2-D DCT coefficients as features. On the PFES, trajectory of a facial image sequence of a person can be calculated in real time. Based on this trajectory, facial expressions can be recognized. Experimental results show the effectiveness of this method.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Forensic age estimation from aspartic acid racemization (AAR): Scrutiny of multiple source samples

We are researching about an age and gender estimation based on wrinkle texture and color of facial images. Preliminary questionnaire (enquete) examination of how effectively the facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. Wrinkles appeared in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research based on this enquete. Based on this preliminary consideration, an image processing algorithm for wrinkle modeling was proposed in this paper. In addition, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing. An efficient interface for displaying the relationships among keywords and facial images has been introduced with 2D/3D-feature space defined by the indexing.

This study aimed to validate Lee's age estimation method and assess the 18-year threshold in Korean and Japanese populations. We evaluated the maxillary and mandibular second (M2) and third molars (M3) in 2657 orthopantomograms of the Korean and Japanese populations aged 15-23 years (19.47±2.62 years for Koreans, 19.31±2.60 years for Japanese), using Demirjian's criteria. Dental age was estimated, and correlations between chronological and dental ages were analyzed. Classification performance was calculated based on the 18-year threshold. The relationship between developmental stage and chronologic age was analyzed using multiple linear regression. Our results revealed that Lee's method was appropriate for estimation in the Korean population. When the Lee's method was applied to the Japanese population, a lower value of correlation coefficients between estimated and chronological age, and lower specificity were observed. Population differences were observed predominantly in the stages of root development (stages F and G) of M2s and M3s in both jaws and more frequently in females than in males. In the multiple linear regression between developmental stage and chronological age, lower values of adjusted r2 were observed in the Japanese population than in the Koreans. In conclusion, the Lee's method derived from the Korean population data might be unsuitable for Japanese juveniles and adolescents. To support the findings of this study, future studies with samples from multiple institutions should be conducted. Future studies with larger sample sizes are also warranted to improve the accuracy of dental age estimation and confirm the developmental pattern of teeth in the Japanese population.

The exact age estimation is often treated as a classification problem; while it can be formulated as a regression problem. In this article, three different classifiers based on KNN classifier's concept for facial age estimation were designed and developed to achieve high efficiency calculation of facial age estimation. In the first classifier, we adopt KNN-distance approach to calculate minimum distance between test face image and all instances belong to the class that has the highest number of nearest samples. Additionally, in the second classifier a modified-KNN version was proposed and the classifier scoring results interpolated to calculate the exact age estimation. Furthermore, KNN-regression classifier as third classifier that used to combine the classification and regression approaches to improve the accuracy of the age estimation system. Moreover, we compared age estimation errors under two situations: case 1, age estimation is performed without discrimination between males and females (gender unknown); and case 2, age estimation is performed for males and females separately (gender known). Results of experiments conducted on well know benchmark FG-NET Database show the effectiveness of the proposed approach.

The process of aging causes significant alterations in the facial appearance of individuals. When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics. Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals. We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images. We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle. Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past. The methodology presented has also been used for designing a face recognition system, robust to aging variation. In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated. Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved.

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this paper, we propose (and compare) two methods for learning face image quality based on target face quality values from: 1) human assessments of face image quality (matcher-independent) and 2) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, Labeled Faces in the Wild and IARPA Janus Benchmark-A (IJB-A), which both contain facial variations encompassing a multitude of quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the false non-match rate at 1% false match rate by at least 13% for two face matchers (a commercial off-the-shelf matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To the best of our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.