A Semi-Supervised Approach to Perceived Age Prediction from Face Images

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.

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 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.

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.

In this paper, we propose age and gender estimation system by vairous features. Age and gender has a lot of characteristics. These characteristics are one of the difficult cognitive process in human interaction. If we can extract the important feature of this cognitive process, it is considered that the age and gender estimation by the machine becomes possible. Therefore, we propose a method of age and gender feature extraction and estimation using the face image. In this paper, the age of the face means apparent-age that is based on the human perception of age. Moreover, person's aging and gender difference appear in the faces. For example, the pigmented spot, the wrinkle, sagging skin, shape, color of skin, and so on. Thus, we extract these several features for age and gender estimation. Furthermore, we estimate a continuous age and gender using a neural network (NN).

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 from facial images is crucial in security, healthcare, and entertainment for applications like age-restricted content filtering and targeted advertising. However, this task is challenging due to pose, illumination, and aging variations. Traditional methods using handcrafted features often lack discriminative power. Deep learning, with its ability to learn complex features, has sparked interest in age estimation. This paper presents a deep learning-based approach for age estimation from frontal face images. It focuses on facial components like eyes, nose, and mouth to capture age-related changes. Augmentation techniques enhance robustness against variations, and a Binary Cascaded CNN with binary weights and activations reduces model complexity. The model transforms augmented components into multimodal features, allowing it to discern age-related changes across domains. Evaluated on FGNET and IMDB-WIKI datasets, the model achieves 99.5% accuracy with an MAE of 1.26 across all age groups. This high accuracy demonstrates the model's effectiveness and potential for real-world age estimation scenarios in diverse applications.

Face recognition has been a major research theme over the last two decades. There are several problems to be solved to improve the performance of face recognition. Such major problems involve appearance variation due to pose, illumination, expression, and aging. In particular, aging includes internal and external factors that cause facial appearance variation and, consequently, it is the most difficult problem to handle. In this paper, we propose a face recognition method that is robust against facial appearance variation due to aging. The proposed method employs segmentation verification of frontal face images that consists of the following three steps. (1) Face image segmentation generates three regional subimages from the input face image. (2) A matching score is calculated using gradient features from a pair consisting of the input image and a registered image for each of the three generated subimages and original (whole face) image. We obtain four matching scores. (3) The verifying classifier evaluates the matching score vector formed of the matching scores calculated for each of the four images and predicts the a posteriori probability that two matching images belong to the same person. The results of an experimental evaluation with the FGNET and MORPH face aging datasets clarify the effectiveness of the proposed method for age invariant face recognition.

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.

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%

The development and distribution of interlayers in sandstones can lead to increased heterogeneity in the formation within the reservoir, which further affects the movement of fluids in the formation. Therefore, it is essential to accurately evaluate the interlayers in sandstones, as the precise evaluation of interlayers in sandstones is of great importance for identifying the distribution of underground fluid systems. The logging data of interlayers are inadequate for traditional Machine Learning training due to their low measurement proportion compared to the conventional layers. In logging data, the amount of data in interlayers is significantly smaller than that of conventional reservoirs. Traditional machine learning models are mostly based on samples with balanced distribution. By contrast, semi-supervised learning requires a small number of labeled samples for learning, and then combines a large number of unlabeled samples for modeling. To verify the feasibility of semi-supervised learning in the identification of interlayers, the Donghe sandstone section of the H oilfield was taken as an example. First, the core analysis results were used to label the logging data; then, to uncover more response information that can characterize the interlayers on the logging curve, multiple features were extracted to construct cross features. Finally, an improved model based on autoencoders—probabilistic autoencoder (PAE)—is proposed to address the issue of interlayer recognition for imbalanced samples. The PAE model can calculate the probability of belonging to a different class for unlabeled samples, and classify new samples according to the maximum probability. Experimental results show that, compared with traditional machine learning methods and ensemble learning methods, PAE achieves higher recognition accuracy and better generalization performance by updating the algorithm, and can be used as a simple and fast method for interlayer recognition. The results of the algorithm demonstrate that the semi-supervised method is of great significance for exploring and developing complex heterogeneous oil reservoirs. The research results are of great importance for exploring and developing complex heterogeneous oil reservoirs.

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 from facial images is emerging as an important research area in recent years due to its promising potential for some computer vision applications. In this paper we propose a novel approach combine the global and local facial features in parallel manner to implement the age estimation. Then after extracting global and local features, these features are integrated for fine classification. In the proposed method, global and local features are extracted by Discrete Fourier Transform (DFT) and Principal Component Analysis (PCA) respectively. We have conducted experiments on a large scale age databases (FGNET). The experimental results are very promising in showing that it is an effective methodKeywordsage estimationglobal featurelocal featureclassifier integration

Age estimation has been widely used and became more and more important, for its usefulness in various applications. However, accurately predict the age for an unlabeled image is difficult, because there are many factors that have impact on the appearance of a person. Some people look younger than his/her true age, while the others look much older. Therefore, predict an age group or a specific age for a facial image is not good enough. In this paper, we propose a new method to estimate the age of facial image into a dynamic range or a discrete age set rather than a single age or age group. Furthermore, we introduce a new measurement, i.e. Confidence Interval/Confidence Level to evaluate the performance of proposed method. Our experimental results show that the proposed method is promising.