Facial Age Estimation with Images in the Wild

To estimate multiple face attributes, independent classifier for each attribute are trained such as facial point detection, gender recognition, and age estimation in the conventional approach. It is inefficient because the computational cost of training and testing increases with the number of tasks. To address this problem, heterogeneous learning is able to train a single classifier to perform multiple tasks. Heterogeneous learning is simultaneously train regression and recognition tasks, thereby reducing both training and testing time. However, it is difficult to obtain equivalent performance for set of single task classifiers due to variance of training error of each task. In this paper, we propose weighted heterogeneous learning of a convolutional neural network with a weighted error function. Our method outperformed the conventional method in terms of facial attribute recognition, especially for regression tasks such as facial point detection, age estimation, and smile ratio 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..

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.

Overcoming occlusions in the wild: A multi-task age head approach to age estimation

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.

Facial age estimation has received a lot of attention for its diverse application scenarios. Most existing studies treat each sample equally and aim to reduce the average estimation error for the entire dataset, which can be summarized as General Age Estimation. However, due to the long-tailed distribution prevalent in the dataset, treating all samples equally will inevitably bias the model toward the head classes (usually the adult with a majority of samples). Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation. However, Long-tailed Age Estimation usually faces a performance trade-off, i.e., achieving improvement in tail classes by sacrificing the head classes. In this paper, our goal is to design a unified framework to perform well on both tasks, killing two birds with one stone. To this end, we propose a simple, effective, and flexible training paradigm named GLAE, which is two-fold. First, we propose Feature Rearrangement (FR) and Pixel-level Auxiliary learning (PA) for better feature utilization to improve the overall age estimation performance. Second, we propose Adaptive Routing (AR) for selecting the appropriate classifier to improve performance in the tail classes while maintaining the head classes. Moreover, we introduce a new metric, named Class-wise Mean Absolute Error (CMAE), to equally evaluate the performance of all classes. Our GLAE provides a surprising improvement on Morph II, reaching the lowest MAE and CMAE of 1.14 and 1.27 years, respectively. Compared to the previous best method, MAE dropped by up to 34%, which is an unprecedented improvement, and for the first time, MAE is close to 1 year old. Extensive experiments on other age benchmark datasets, including CACD, MIVIA, and Chalearn LAP 2015, also indicate that GLAE outperforms the state-of-the-art approaches significantly.

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.

Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0–17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.

Facial age estimation has been widely adopted in the last decade, yet it presents critical privacy issues, such as susceptibility to unauthorized exposure. In contrast, tongue imagery has emerged as a promising alternative to traditional facial age estimation methods, offering improved privacy through reduced identifiability compared to facial imagery. This paper introduces a novel framework for age estimation using tongue imagery, addressing privacy concerns associated with facial recognition technologies. Our method takes advantage of a three-module approach: segmentation, quality control, and prediction, to estimate the age from tongue images. The segmentation module employs a zero-shot inference technique using the Large Language Instructed Segmentation Assistant (LISA) to accurately segment the tongue from its oral cavity images. Subsequent quality assessment of these segmentations is performed in the quality control module, ensuring only high-quality images proceed to the prediction stage. The age prediction module then employs an ensemble of EfficientNet B2 models, fine-tuned and optimized for age prediction from tongue imagery. Our experimental results derived from a newly collected dataset consisting of 9,767 tongue and facial images spanning diverse age groups demonstrate that the proposed method achieves an accuracy comparable to traditional facial age estimation techniques. This study not only provides an alternative approach to facial biometrics for age estimation but also enhances user privacy, making it particularly suitable for privacy-sensitive applications in healthcare and biometric monitoring.

Face detection and landmark localization are usually the fundamental and important steps of facial analysis. The accuracy degradation of any task has impacts on the accuracy and robustness of the downstream analysis. In most facial analysis systems, face detection and landmark detection, as two independent tasks, are predicted sequentially with single-task detectors respectively, which suffer from the higher complexity and poorer consistency. In contrast, multi-task joint face and landmark detectors are more efficient and have more shared features. However, when two tasks are predicted in one model, it is difficult to improve the accuracy of all tasks simultaneously, due to the different requirements of the different targets to be detected. In our opinion, the multi-task detection model, integrating the perception ability of both scale variation and detailed texture information for face and landmark detection, is arduous but important in unconstrained settings. In this paper, a High-Performance joint Face and Landmark detector(HP-FLDet) is proposed, which can predict facial bounding boxes and landmarks in single stage and improve the accuracy of both face detection and landmark localization simultaneously. In this detector, the context inception block and scale-aware multi-task strategies are designed to resolve extreme scale variance and improve the perception ability of detailed texture information. It demonstrates superior performance on both face and landmark detection datasets including AFW, PASCAL Face, FDDB, Wider Face and AFLW. Especially, it achieves SOTA results on the most challenging WIDER FACE dataset with 0.970(Easy), 0.963(Medium), 0.921(Hard) mAP on the test subset, which outperforms all state-of-the-art rivals.

The digital world draws sensitive attention in the field of face recognition and facial age estimation. Despite of various transpiring researches in this domain, invigoration is indeed required. With respect to various challenges, the estimation of age requires consideration of several aspects. The estimation of age can be refined by considering both geometrical measures and texture analysis. The steps to be followed are normalization, face cropping, filtering, feature extraction and finally classification to provide the refined age of the face image. The normalization of face is done using histogram equalization to study the intensity component. From the image the face is cropped. The median filter is applied on the image to detach noise in the image and to maintain the edge details. The requisite features are extracted from the image such as geometric measures, texture details. The structure of texture features and the intensity transitions are analyzed. With these descriptors, the age of the image is classified.

Multilinear subspace analysis (MSA) is a promising methodology for pattern-recognition problems due to its ability in decomposing the data formed from the interaction of multiple factors. The MSA requires a large training set, which is well organized in a single tensor, which consists of data samples with all possible combinations of the contributory factors. However, such a "complete" training set is difficult (or impossible) to obtain in many real applications. The missing-value problem is therefore crucial to the practicality of the MSA but has been hardly investigated up to present. To solve the problem, this paper proposes an algorithm named M(2)SA, which is advantageous in real applications due to the following: 1) it inherits the ability of the MSA to decompose the interlaced semantic factors; 2) it does not depend on any assumptions on the data distribution; and 3) it can deal with a high percentage of missing values. M(2)SA is evaluated by face image modeling on two typical multifactorial applications, i.e., face recognition and facial age estimation. Experimental results show the effectiveness of M(2) SA even when the majority of the values in the training tensor are missing.

This paper has presented a method of facial age estimation using a hybrid of Support Vector Machines (SVMs) and Fuzzy Logic (FL). The proposed method has taken facial features from wrinkles and skin color on the human face to estimate the age group and age in point. SVMs was used to estimate the five age-groups of human age. Then, FL was implemented to estimate the age in point corresponding in each group resulting from SVMs. For performance evaluation, k-fold cross validation was carried out using FG-NET and PAL databases consisting of 700 and 500 faces, respectively. The proposed method was evaluated in comparison with five advanced methods in literature. The results showed that the proposed method provided 88.84% and 90.88% of accuracy in aging group estimation in FG-NET and PAL databases, respectively. In addition, the proposed method reported 4.81 and 3.12 for MAE (mean absolute error) for point age estimation using FG-NET and PAL, respectively. In this regard, the proposed method provided the higher performance on accuracy and MAE superior to the compared methods.

Facial age estimation has attracted considerable attention owing to its great potential in applications. However, it still falls short of reliable age estimation due to the lack of sufficient training data with accurate age labels. Using conventional semi-supervised methods to exploit unlabeled data appears to be a good solution, but it does not yield sufficient performance gains while significantly increasing training time. Therefore, to tackle these problems, we present a Divergence-driven Consistency Training (DCT) method for enhancing both efficiency and performance in this paper. Following the idea of pseudo-labeling and consistency regularization, we assign pseudo labels predicted by the teacher model to unlabeled samples and then train the student model on labeled and unlabeled samples based on consistency regularization. Based on this, we propose two main promotions. The first is the Efficient Sample Selection (ESS) strategy, which is based on the Divergence Score to select effective samples from massive unlabeled images to reduce the training time and improve efficiency. The second is Identity Consistency (IC) regularization as the additional loss function, which introduces a high dependency of aging traits on a person. Moreover, we propose Local Prediction (LP), which is a plug-and-play component, to capture local semantics. Extensive experiments on multiple age benchmark datasets, including CACD, Morph II, MIVIA, and Chalearn LAP 2015, indicate DCT outperforms the state-of-the-art approaches significantly.

In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holistic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Contourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age estimation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.