Development of a Deep Learning Model for Age Estimation Utilizing Multimodal Feature and Augmented Analysis

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.

3D face modeling from 2D face images is of significant importance for face analysis, animation and recognition. Previous research on this topic mainly focused on 3D face modeling from a single 2D face image; however, a single face image can only provide a limited description of a 3D face. In many applications, for example, law enforcement, multi-view face images are usually captured for a subject during enrollment, which makes it desirable to build a 3D face texture model, given a pair of frontal and profile face images. We first determine the correspondence between un-calibrated frontal and profile face images through facial landmark alignment. An initial 3D face shape is then reconstructed from the frontal face image, followed by shape refinement utilizing the depth information provided by the profile image. Finally, face texture is extracted by mapping the frontal face image on the recovered 3D face shape. The proposed method is utilized for 2D face recognition in two scenarios: (i) normalization of probe image, and (ii) enhancing the representation capability of gallery set. Experimental results comparing the proposed method with a state-of-the-art commercial face matcher and densely sampled LBP on a subset of the FERET database show the effectiveness of the proposed 3D face texture model.

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.

Age estimation from facial images is one of the most popular fields of research concerning deep learning and convolutional neural networks. However, there are several factors influencing the final accuracy that require special consideration, and in this research, we examine how gender classification affects age estimation. We use a predefined version of the MobileNetV2 convolutional neural network and train it on the CASIAWebFace dataset which we augmented with our private dataset called AgeCFBP. For the purpose of testing the network performance, we used the FG-NET dataset. The results of our experiments showed that gender pre-classification has a measurable impact on age estimation in both male and female population by decreasing Mean Absolute Error (MAE) metric, which might lead to enhanced applications in real-world scenarios, such as biometric authentication, security systems, human-computer interaction, and age-restricted content access control.

Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include five stages: gray scale image, noise removable, face detection, image size normalization and clipping process. The second phase is a data mining process which includes three stages: feature extraction, feature selection and classification using j48 classifier. The third phase includes two stages, estimation and evaluation. FG-NET dataset is used which is divided into three classes; first class represents (3-7), (26-30) ages and this class represents the ages from 3 to 7 years and from 26 to 30 years because this class have four attributes from any one of this images, second class represents (8-25) ages and this class represents the ages from 8 to 25 years because this class have five attributes from any one of this images, last class represents (31-50) ages and have nine attributes from any one of this images . The Experimental results illustrate that the proposed system can give results with high precision and low time complexity. The practical evaluation of the proposed system gives accuracy up to 89.13 % with time taken of 0.023.

Recently there has been an urgent need to identify the ages from their personal pictures and to be used in the field of security of personal and biometric, interaction between human and computer, security of information, law enforcement. However, in spite of advances in age estimation, it stills a difficult problem. This is because the face old age process is determined not only by radical factors, e.g. genetic factors, but also by external factors, e.g. lifestyle, expression, and environment. This paper utilized machine learning technique to intelligent age estimation from facial images using support vector machine (SVM) on FG_NET dataset. The proposed work consists of three phases: the first phase is image preprocessing include four stages: grayscale image stage, histogram equalization stage, face detection stage has been carried out using viola jones algorithm, it comprises for four steps namely: Haar like Feature, integral image, Adaboost training, and cascading classifier, the last stage of image preprocessing phase is cropping and resize stage. The second phase is data mining include two stages: feature extraction stage using linear discriminate analysis and machine learning stage using support vector machine. The last phase is age estimation and evaluation. The FG-net dataset is used which divided into seven classes in order to has been became increased accuracy and reduce the execution time, the first class represents 3-7 years, the second class represents 8-13 years, the third class represents 14-19 years, the forth class represents 20-25 years, the fifth class represents 26-30 years, the sixth class represents 31-40 years and the seven class represents 41-50 years. Then, the seven classes are combined into three classes depending on the number of features. The Experimental results display that the proposed system can grant high accuracy. The practical evaluation of the proposed system gives accuracy is 84%.

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.

Age related changes in thyroid and cricoid cartilages: An autopsy based radiological analysis

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.

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, facial poses, and illumination conditions. In this paper, we apply a recently-proposed machine learning technique called covariate shift adaptation to alleviating lighting condition change between laboratory and practical environment. Through real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

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

Age estimation from facial images is a challenging computer vision task with diverse real-world applications. This paper proposes a novel hybrid architecture that integrates ConvNeXt, a state-of-the-art convolutional neural network (CNN), with a Vision Transformer (ViT) to leverage the complementary strengths of localized feature extraction and global attention. The proposed ConvNeXt-ViT hybrid is evaluated on benchmark age estimation datasets (MORPH II, CACD, AFAD, and IMDB-Clean) and achieves superior performance in terms of mean absolute error (MAE) compared to existing approaches. We explore various architectural configurations, use pre-trained models to address data limitations, and apply advanced training strategies and regularization techniques. Comprehensive ablation studies highlight the impact of each component and training strategy, particularly the importance of adapted attention mechanisms within the CNN backbone to focus on age-relevant facial features. The results demonstrate that the ConvNeXt-ViT hybrid not only outperforms traditional approaches but also provides a robust foundation for future research in age estimation and related visual tasks.

646 Analysis of statistics for facial image color between ages

We address the problem of perceived age estimation from face images, and propose a new semi-supervised approach involving two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster-representative samples for covering as many clusters as possible. This simple sampling strategy allows us to boost the performance of a manifold-based semi-supervised learning method only with a relatively small number of labeled samples. The second contribution is to take the heterogeneous characteristics of human age perception into account. It is rare to misjudge the age of a 5-year-old child as 15 years old, but the age of a 35-year-old person is often misjudged as 45 years old. Thus, magnitude of the error is different depending on subjects’ age. We carried out a large-scale questionnaire survey for quantifying human age perception characteristics, and propose to utilize the quantified characteristics in the framework of weighted regression. Consequently, our proposed method is expressed in the form of weighted least-squares with a manifold regularizer, which is scalable to massive datasets. Through real-world age estimation experiments, we demonstrate the usefulness of the proposed method.