Automatic Age Estimation Research Articles

A New Cost Function Combining Deep Neural Networks (DNNs) and l2,1-Norm with Extraction of Robust Facial and Superpixels Features in Age Estimation

Automatic age estimation from unconstrained facial images is a challenging task and it recently has gained much attention due to its wide range of applications. In this paper, we propose a new model based on convolutional neural networks (CNNs) and l2,1-norm to select age-related features for the age estimation task. A new cost function is proposed. To learn and train the new model, we provide the analysis and the proof for the convergence of the new cost function to solve minimization problem of deep neural networks (DNNs) and the l2,1-norm. High-level features are extracted from the facial images by using transfer learning, since there are currently not enough large age databases that can be used to train a deep learning network. Then, the extracted features are fed to the proposed model to select the most efficient age-related features. In addition, a new system that is based on DNN to jointly fine-tune two different DNNs with two different feature sets is developed. Experimental results show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database.

A Review of Different Techniques of Age Estimation from Human Face

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

Improvement of age estimation using an efficient wrinkles descriptor

Lately, automatic age estimation from facial images is in demand because of the usage of this technique in different fields including security, demographic analysis, access control and vending machines control. However, age estimation is difficult to conduct due to the aging process features’ evolution complexity, such as the face shape and skin wrinkles. In this context, we propose a new descriptor called Local Matched Filter Binary Pattern (LMFBP) designed specifically for the detection and extraction of skin wrinkles. This descriptor is based on exploiting both the Matched Filter and the texture operator Local Binary Pattern (LBP). The Matched Filter handles the detection of wrinkles using template matching between the approximate shape of wrinkles and the face image patches. Furthermore, the LBP operator encodes the response of the Matched Filter into pattern codes to build the histogram of skin aging feature. The fusion of local features provided by the LMFBP with the global features of the appearance enabled us to propose a new age estimation method. In this method, we adopted the hierarchical approach in the learning phase, in order to consider the varying aging process from one age stage to another. The proposed age estimation method has been tested on both FGnetAD, HQfaces and PAL datasets, and the results provided are 4.95, 3.65 and 5.33 in terms of MAE, respectively. These results prove the efficiency of the proposed approach when compared to the state-of-the-art age estimation methods.

Reducing acquisition time for MRI-based forensic age estimation

Radiology-based estimation of a living person’s unknown age has recently attracted increasing attention due to large numbers of undocumented immigrants entering Europe. To avoid the application of X-ray-based imaging techniques, magnetic resonance imaging (MRI) has been suggested as an alternative imaging modality. Unfortunately, MRI requires prolonged acquisition times, which potentially represents an additional stressor for young refugees. To eliminate this shortcoming, we investigated the degree of reduction in acquisition time that still led to reliable age estimates. Two radiologists randomly assessed original images and two sets of retrospectively undersampled data of 15 volunteers (N = 45 data sets) applying an established radiological age estimation method to images of the hand and wrist. Additionally, a neural network-based age estimation method analyzed four sets of further undersampled images from the 15 volunteers (N = 105 data sets). Furthermore, we compared retrospectively undersampled and acquired undersampled data for three volunteers. To assess reliability with increasing degree of undersampling, intra-rater and inter-rater agreement were analyzed computing signed differences and intra-class correlation. While our findings have to be confirmed by a larger prospective study, the results from both radiological and automatic age estimation showed that reliable age estimation was still possible for acquisition times of 15 seconds.

Automatic Age Estimation Based on LBP and GLCM Features Using SVM

Face is generally considered as the reference frame of mind. Therefore, to estimate the feeling of the mind, many authors have considered the emotions from the facial expressions into consideration to identify the state of mind of an individual. Hence in this article we proposed a methodology for automatic age estimation based on Local Binary Pattern (LBP) and Grey Level Co- Occurrence Matrix (GLCM). The facial features are extracted using LBP and GLCM and these features are given as input’s to the Support Vector Machine (SVM) for age estimation. The experimentation on proposed method is carried out using FG-NET database and Mean Absolute Error (MAE) is calculated to compare the proposed method with state-of-the-art algorithms. Finally, the proposed methodology demonstrates the classification accuracy above 88%.

Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation

Automatic age estimation is an important yet challenging problem. It has many promising applications in social media. Of the existing age estimation algorithms, the personalized approaches are among the most popular ones. However, most person-specific approaches rely heavily on the availability of training images across different ages for a single subject, which is usually difficult to satisfy in practical application of age estimation. To address this limitation, we first propose a new model called Orthogonal Gaussian Process (OGP), which is not restricted by the number of training samples per person. In addition, without sacrifice of discriminative power, OGP is much more computationally efficient than the standard Gaussian Process. Based on OGP, we then develop an effective age estimation approach, namely anisotropic OGP (A-OGP), to further reduce the estimation error. A-OGP is based on an anisotropic noise level learning scheme that contributes to better age estimation performance. To finally optimize the performance of age estimation, we propose a multifeature A-OGP fusion framework that uses multiple features combined with a random sampling method in the feature space. Extensive experiments on several public domain face aging datasets (FG-NET, MORPH Album1, and MORPH Album 2) are conducted to demonstrate the state-of-the-art estimation accuracy of our new algorithms.

Automatic age estimation from facial profile view

In recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side‐view of face images. Here the authors exploit this by using a pretrained deep residual neural network to extract features, and then utilise a sparse partial least‐squares regression approach to estimate ages. Despite having less information as compared with frontal images, the results show that the extracted deep features achieve a promising performance.

Age classification with deep learning face representation

Automatic age estimation from facial images is challenging not only for computers, but also for humans in some cases. Therefore, coarse age groups such as children, teen age, adult and senior adult are considered in age classification, instead of evaluating specific age. In this paper, we propose an approach that provides a significant improvement in performance on benchmark databases and standard protocols for age classification. Our approach is based on deep learning techniques. We optimize the network architecture using the Deep IDentification-verification features, which are proved very efficient for face representation. After reducing the redundancy among the large number of output features, we apply different classifiers to classify the facial images to different age group with the final features. The experimental analysis shows that the proposed approach outperforms the reported state-of-the-arts on both constrained and unconstrained databases.

Age-Variation Face Recognition Based on Bayes Inference

Studies have discovered that face recognition will benefit from age information. However, since the age estimation is unstable in practice, it is still an open question how to improve face recognition with help of automatic age estimation techniques. This paper presents to improve the performance of face recognition by automatic age estimation. The main contribution is a new age-variational face recognition algorithm based on Bayesian framework (FRAB). By introducing the age estimation result as a prior, the recognition problem is divided into several age-specific sub-problems. As a result, the proposed algorithm leads to two algorithms according to how the age is given. The first one is FRAB-AE, which introduces age estimation result as the age prior. The second one is FRAB-GT, which considers that the ground truth of age information is given. Experimental results are conducted on FG-NET and Morph datasets to evaluate the performance of the proposed framework. It shows that the proposed algorithms is able to make use of age priors to improve the face recognition.

Expression-Invariant Age Estimation Using Structured Learning.

In this paper, we investigate and exploit the influence of facial expressions on automatic age estimation. Different from existing approaches, our method jointly learns the age and expression by introducing a new graphical model with a latent layer between the age/expression labels and the features. This layer aims to learn the relationship between the age and expression and captures the face changes which induce the aging and expression appearance, and thus obtaining expression-invariant age estimation. Conducted on three age-expression datasets (FACES , Lifespan and NEMO ), our experiments illustrate the improvement in performance when the age is jointly learnt with expression in comparison to expression-independent age estimation. The age estimation error is reduced by 14.43, 37.75 and 9.30 percent for the FACES, Lifespan and NEMO datasets respectively. The results obtained by our graphical model, without prior-knowledge of the expressions of the tested faces, are better than the best reported ones for all datasets. The flexibility of the proposed model to include more cues is explored by incorporating gender together with age and expression. The results show performance improvements for all cues.

Investigating the Effects of Facial Regions to Age Estimation

Aging process causes evident alterations on human facial appearance. Real world age progression on human face is personalized and related with many factors such as, genetics, living style, eating habits, facial expressions, climate etc. The wide degree of variations on facial appearance of different individuals affects the age estimation performance. In accordance with these facts discovering the aging information contained in facial regions is an important issue in automatic age estimation. Thus the facial regions emphasizing the aging information can be used for more accurate age estimation. In this context, age estimation performances of facial regions (eye, nose, mouth and chin, cheeks and sides of mouth) are investigated in this paper. For this purpose, an age estimation method is designed to produce an estimate of the age of a subject by using the texture features extracted from facial regions. In this method the facial images are warped into the mean shape thus variations of head pose and scale are eliminated and the texture information of facial images are aligned. Then the holistic and spatial texture features are extracted from facial regions using Local Phase Quantization (LPQ) texture descriptor, robust to blur, illumination and expression variations. After the low dimensional representation of these features, a linear aging function is learned using multiple linear regression. In the experiments FGNET and PAL databases are used to evaluate the age estimation accuracies of facial regions i.e. eye, nose, mouth and chin, cheek and sides of mouth, separately. The results have shown that the eye region carries the most significant information for age estimation. Also the mouth and chin, cheek regions are effective in the prediction of age. The results also have shown that, using the spatial texture features enhances the discriminative power of the texture descriptor and thus increases the estimation accuracy.

Fusion for Image Based Human Age Estimation

AbstractHuman faces act as essential visual signal and reveal significant amounts of nonverbal information in human-to-human communication. In an automatic age estimation system, shape-based and texture-based features extracted from faces forms the basis of age estimation. Human age estimation with facial image analysis as an automated method houses many potential real‐world factors. This paper presents an automated age calculation framework with the help of Support Vector Regression (SVR) strategy and it highlights feature extraction with Gray Level Co-occurrence Matrix (GLCM), and Active Appearance Models (AAMs) to evaluate human age. A fused feature technique and a SVR based Cuckoo Search (CS) is proposed in order to reduce error in age estimation.

Automatic age estimation based on deep learning algorithm

Automatic age estimation has attracted much attention due to its potential applications. Most of the proposed approaches have mainly used low-level handcraft features to encode facial age related visual information and train an age estimation model. In this paper, we focus on age classification task in which face image is assigned to a label that represents an age range. We proposed a deep learning based framework for age classification task. In our proposed algorithm, Deep Convolutional Neural Networks (Deep ConvNets) are used to extract high-level complex age related visual features and predict age range of input face image. Due to lack of age labeled face images, we use the transfer learning strategy to train the Deep ConvNets. In addition, to describe the relationships between labels that compose an ordered sequence, we define a new loss function in the training process of age classification task. The experiments are conducted on a widely used age estimation dataset-Images of Groups of People. The experimental results demonstrate the excellent performance of our proposed algorithm against the state-of-the-art methods.

Artificial Neural Networks and Gene Expression Programing based age estimation using facial features

This work is about estimating human age automatically through analysis of facial images. It has got a lot of real-world applications. Due to prompt advances in the fields of machine vision, facial image processing, and computer graphics, automatic age estimation via faces in computer is one of the dominant topics these days. This is due to widespread real-world applications, in areas of biometrics, security, surveillance, control, forensic art, entertainment, online customer management and support, along with cosmetology. As it is difficult to estimate the exact age, this system is to estimate a certain range of ages. Four sets of classifications have been used to differentiate a person’s data into one of the different age groups. The uniqueness about this study is the usage of two technologies i.e., Artificial Neural Networks (ANN) and Gene Expression Programing (GEP) to estimate the age and then compare the results. New methodologies like Gene Expression Programing (GEP) have been explored here and significant results were found. The dataset has been developed to provide more efficient results by superior preprocessing methods. This proposed approach has been developed, tested and trained using both the methods. A public data set was used to test the system, FG-NET. The quality of the proposed system for age estimation using facial features is shown by broad experiments on the available database of FG-NET.

What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents

Background: Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Automatic, software-based methods additionally promise improved estimation objectivity.Aim: To investigate how informative automatically selected image features are regarding their ability to discriminate age, by exploring a recently proposed software-based age estimation method for MR images of the left hand and wrist.Subjects and methods: One hundred and two MR datasets of left hand images are used to evaluate age estimation performance, consisting of bone and epiphyseal gap volume localisation, computation of one age regression model per bone mapping image features to age and fusion of individual bone age predictions to a final age estimate.Results: Quantitative results of the software-based method show an age estimation performance with a mean absolute difference of 0.85 years (SD = 0.58 years) to chronological age, as determined by a cross-validation experiment. Qualitatively, it is demonstrated how feature selection works and which image features of skeletal maturation are automatically chosen to model the non-linear regression function.Conclusion: Feasibility of automatic age estimation based on MRI data is shown and selected image features are found to be informative for describing anatomical changes during physical maturation in male adolescents.

Relative Attribute SVM+ Learning for Age Estimation.

When estimating age, human experts can provide privileged information that encodes the facial attributes of aging, such as smoothness, face shape, face acne, wrinkles, and bags under-eyes. In automatic age estimation, privileged information is unavailable to test images. To overcome this problem, we hypothesize that asymmetric information can be explored and exploited to improve the generalizability of the trained model. Using the learning using privileged information (LUPI) framework, we tested this hypothesis by carefully defining relative attributes for support vector machine (SVM+) to improve the performance of age estimation. We term this specific setting as relative attribute SVM+ (raSVM+), in which the privileged information enables separation of outliers from inliers at the training stage and effectively manipulates slack variables and age determination errors during model training, and thus guides the trained predictor toward a generalizable solution. Experimentally, the superiority of raSVM+ was confirmed by comparing it with state-of-the-art algorithms on the face and gesture recognition research network (FG-NET) and craniofacial longitudinal morphological face aging databases. raSVM+ is a promising development that improves age estimation, with the mean absolute error reaching 4.07 on FG-NET.

Automatic age estimation in adults by analysis of canine pulp/tooth ratio: Preliminary results

The estimation of age of adult individuals usually employs methods based on age-related changes in the human skeleton. The main aim of this study was to assess dental age through analysis of images from peri-apical x-rays based on a new algorithm for automatic estimation of pulp and tooth area. The algorithm was tested on a sample of 40 canines of males and 30 of females from subjects between the ages of 20 and 70.

Estimating Age Value from Super-Resolution Reconstruction Facial Images

Automatic age estimation based on facial images is important but challenging in face recognition research. A Super-Resolution Reconstruction algorithm was proposed to implement the age estimation of facial images, which cut the facial image into small pieces. Then after building high resolution images by using the Super-Resolution Reconstruction algorithm, the RBF neural network was used to train and test these high resolution images. At last, the classifier ensemble with genetic algorithm was used to estimate age information. Finally, experimental results demonstrate that it is an effective method.

Automatic Age Estimation System for Face Images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications.

Age Group Estimation Using Face Angle

Recognition of the most facial variations, such as identity, expression and gender, has been extensively studied. Automatic age estimation has rarely been explored. With age progression of a human the face angle changes. This paper concerns with providing a methodology to estimate age groups using face features. The proposed method is based on the face triangle which has three coordinate points between left eye ball, right eyeball and mouth point. The face angle between left eyeball, mouth point and right eyeball estimates the age of a human. However, very few studies have been done on age classification or age estimation. This paper proves that face angle can estimate and classify human age according to face features extracted from human facial images. Age ranges are classified into five categories. Those are child (up to 17 years), young (18 to 25 years), adult (26 to 35 years), middle aged (36 to 45 years) and old (more than 45 years). The obtained results were significant. This paper can be used for predicting future faces, classifying gender, and expressions from facial images.