Human age estimation ability and factors affect the estimation

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.

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.

In human age estimation, an exact age or age group is assigned automatically to a given facial image. Due to changes in facial features caused by wrinkles and muscle sag with the increase in age, age estimation becomes difficult and complex. Variability in facial expressions, illumination conditions, environment, pose, plastic surgery, occlusion, image quality, and facial makeup gives rise to some of the challenges that affect the accuracy of age estimation. With the newly developing real-time and age-related applications, there is an increasing demand for more accurate estimation of age. Several methodologies have been developed by researchers to handle this requirement. The work reported here represents an exhaustive survey on face age estimation. Discussed here are five major models of age image representation and various age learning algorithms, a review of the existing aging databases has been made, and the latest state-of-the-art methods for the estimation of age are summarized.

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.

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.

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.

Age estimation using face images has been widely employed across various fields. Because the characteristics of face images usually vary greatly depending on race, camera type, lighting, and other environmental factors, the recognition ability of untrained heterogeneous face image databases is not accurate by previous methods. Therefore, various attempts have been made where different heterogeneous databases were combined to enable training; however, the training time is extended and diverse environmental variables in databases cannot be sufficiently trained. To address these issues, this study proposes modified cycle-consistent generative adversarial network (CycleGAN) that generates an even distribution of heterogeneous face data, and an age-estimation method that is effective for heterogeneous data based on comparative CNN for age estimation (CCNNAE). In addition, we propose the method of reducing the errors caused by image transformation in modified CycleGAN through adaptive selection between transformed and original face images for the input to CCNNAE, which is based on age similarity between the transformed face image and the original face image. Experiments with two open databases, MORPH and MegaAge databases showed that our method outperformed the state-of-the art methods.

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 of image features derived by the Orthogonal Locality Preserved Projections (OLPP) and the Sobel Edge Detector (SED) algorithms. The proposed method was tested using the noncommercial version of the MORPH2 database. For male faces, the age estimation results for the MSAE method achieved above 90% of average accuracy for the 26-34 years of age range, and above 80% for 16-26 and 70% for 34-50 years of age. Similar trends were observed for female faces; however the accuracy was slightly lower due to smaller number of valid images.

International audience

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 features. As global features, Active Appearance Models (AAM) was mainly used for age estimation in previous works. However, AAM is not enough to represent local features such as wrinkle and skin. Therefore, the research about local features is required. In previous works, local features were generally used to determine age group rather than detailed age, and the comparative studies about various local features extraction methods were not conducted. In this paper, the performances of sobel filter, difference image between original and smoothed image, ideal high pass filter (IHPF), gaussian high pass filter (GHPF), Haar and Daubechies discrete wavelet transform (DWT) are compared for extracting local features and detailed age estimation is performed by Support Vector Regression (SVR) on BERC and PAL aging database. The experimental results show that local features can be used for detailed age estimation and GHPF gives a better performance than other methods.

D2C: Deep cumulatively and comparatively learning for human age estimation

Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.

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..

Age progression is generally indicated by skin texture. The skin texture changes with age progression of a person. This paper provides a technique to estimate the age of a person by analysis of wrinkle on face images. Wrinkle areas are detected and features are extracted from face image. Based on feature values of each face image is clustered using fuzzy c-means. Each face image is assigned to a cluster based on its maximum membership value. Age estimation is done using their membership values and average ages of each cluster. Total 20 face images are taken for training the system and 120 face images are tested. The obtained results are significant and accuracy up to 87.5%

Annotating age classes for humans’ facial images according to their appearance is very challenging because of dynamic person-specific ageing pattern, and thus leads to a set of unreliable apparent age labels for each image. For utilising ambiguous label annotations, an intuitive strategy is to generate a pseudo age for each image, typically the average value of manually-annotated age annotations, which is thus fed into standard supervised learning frameworks designed for chronological age estimation. Alternatively, inspired by the recent success of label distribution learning, this paper introduces a novel concept of ambiguous label distribution for apparent age estimation, which is developed under the following observations that (1) soft labelling is beneficial for alleviating the suffering of inaccurate annotations and (2) more reliable annotations should contribute more. To achieve the goal, label distributions of sparse age annotations for each image are weighted according to their reliableness and then combined to construct an ambiguous label distribution. In the light, the proposed learning framework not only inherits the advantages from conventional learning with label distribution to capture latent label correlation but also exploits annotation reliableness to improve the robustness against inconsistent age annotations. Experimental evaluation on the FG-NET age estimation benchmark verifies its effectiveness and superior performance over the state-of-the-art frameworks for apparent age estimation.