Comprehensive review on facial based human age estimation
TL;DR: Analysis of earlier techniques proposed by researchers for facial based age estimation is presented and different feature extraction and estimator learning methods used in this domain are also discussed.
Abstract: Recently facial based age estimation has become increasingly important because of many potential real time applications. Age estimation is predicting someone's age by analyzing his/her biometric trait such as bone density, dental structure or face. Amongst these face is important trait so facial based age estimation has become more popular due to its vast real time applications. Age estimation is defined as to label the face image automatically with the exact age or age group. Estimating age from images has been one of the most challenging problems within the field of facial analysis due to uncontrollable nature of the aging process, high variance of observations within the same age range, lighting, facial expressions, pose, occlusion, blur, camouflage due to beards, moustache, glasses, makeup and the difficulty to gather complete and sufficient training data. This paper presents analysis of earlier techniques proposed by researchers for facial based age estimation. Different feature extraction and estimator learning methods used in this domain are also discussed.
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01 Jan 2020
TL;DR: How Convolutional Neural Network can be used for age estimation and the advantage of using deep CNNs over traditional methods are discussed and the article aims to evaluate various databases and algorithms used forAge estimation using facial images and dental images.
Abstract: Forensic age estimation is usually requested by courts, but applications can go beyond the legal requirement to enforce policies or offer age-sensitive services. Various biological features such as the face, bones, skeletal and dental structures can be utilised to estimate age. This article will cover how modern technology has developed to provide new methods and algorithms to digitalise this process for the medical community and beyond. The scientific study of Machine Learning (ML) have introduced statistical models without relying on explicit instructions, instead, these models rely on patterns and inference. Furthermore, the large-scale availability of relevant data (medical images) and computational power facilitated by the availability of powerful Graphics Processing Units (GPUs) and Cloud Computing services have accelerated this transformation in age estimation. Magnetic Resonant Imaging (MRI) and X-ray are examples of imaging techniques used to document bones and dental structures with attention to detail making them suitable for age estimation. We discuss how Convolutional Neural Network (CNN) can be used for this purpose and the advantage of using deep CNNs over traditional methods. The article also aims to evaluate various databases and algorithms used for age estimation using facial images and dental images.
2 citations
Cites background from "Comprehensive review on facial base..."
...While a human face reflects significant amount of communicative information and facets about a person including gender, identity, ethnicity, expression and age, which humans have a capability to detect at a glance, there is a growing expectation that digital systems will have similar capabilities and recognition accuracy seamlessly [16-18]....
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01 Jan 2020
TL;DR: This study shows that due to inclusion of deep belief network performance is excelled in age estimation, which has shown superior performance as compared to other classification models.
Abstract: Facial based human age estimation has attracted lot of attention nowadays. Age estimation has become quite challenging task due to variation in lighting conditions, poses, and facial expression. Despite so much research in facial based human age estimation still there is room to improve performance. To improve accuracy we present age estimation using deep belief network. Deep belief network have shown superior performance as compared to other classification models. Success of deep belief network lies in contrastive divergence algorithm. Facial images passes though viola johns facial detection algorithm, once face is detected facial featured are extracted using active appearance and scattering transform feature method. These feature extraction model not only extracts geometric features but also extracts texture features. Subsequently deep belief network classification model is built on partitioned training images and evaluated on testing images. We performed experimentation on training images. Dataset and results are obtained by varying training percentages. Compared to other age estimation models we achieved low mean absolute error of 4.95 for 70% training images dataset. This study shows that due to inclusion of deep belief network performance is excelled.
2 citations
2 citations
30 Jun 2020
TL;DR: Main purpose of this work is improving facial based age estimation accuracy by building a Neural Network (NN), which has shown superior performance for trainlm algorithm as compared to trainscg and traingdm algorithm.
Abstract: Facial based human age estimation has become popular now a days due to tremendous increase in real time applications. Age estimation process comes with various challenges such as variation in lighting conditions, poses and facial expression. Performance of age estimation is evaluated with the help of measure ‘Mean Absolute Error’ (MAE). Main purpose of this work is improving facial based age estimation accuracy by building a Neural Network (NN). Beauty of NN is it learns nonlinear features of input data efficiently. For building NN model input images undergoes preprocessing, feature extraction, training and testing phases. NN model is built by providing training using various training algorithms. For prediction of age estimation neural network have shown superior performance for trainlm algorithm as compared to trainscg and traingdm algorithm. Experimentation are performed on partitioned training images and evaluated on testing images. We achieved low mean absolute error of 4.53 for 70 percent training images dataset.
1 citations
Additional excerpts
...DeepID crops 60 patches from face image and each patch is fed to independent network [14]....
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TL;DR: In this article , a novel age estimation system from the combination of a genetic algorithm and a back propagation (BP)-trained artificial neural network (ANN) and using the local binary pattern feature extraction technique (LBGANN) targeted at black faces was developed.
Abstract: Abstract Age estimation is the ability to predict the age of an individual based on facial clues. This could be put to practical use in underage voting detection, underage driving detection, and overage sportsmen detection. To date, no popular automatic age estimation system has been developed to target black faces. This study developed a novel age estimation system from the combination of a genetic algorithm and a back propagation (BP)-trained artificial neural network (ANN) and using the local binary pattern feature extraction technique (LBGANN) targeted at black faces. The system was trained with a predominantly black face database, and the result was compared against that of a standard ANN system (LBANN). The results showed that the developed system LBGANN outperformed the LBANN in terms of the correct classification rate.
1 citations
References
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Abstract: We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
6,200 citations
02 Jun 1998
TL;DR: A novel method of interpreting images using an Active Appearance Model (AAM), a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example.
Abstract: We demonstrate a novel method of interpreting images using an Active Appearance Model (AAM). An AAM contains a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example. During a training phase we learn the relationship between model parameter displacements and the residual errors induced between a training image and a synthesised model example. To match to an image we measure the current residuals and use the model to predict changes to the current parameters, leading to a better fit. A good overall match is obtained in a few iterations, even from poor starting estimates. We describe the technique in detail and give results of quantitative performance tests. We anticipate that the AAM algorithm will be an important method for locating deformable objects in many applications.
3,905 citations
TL;DR: In this paper, the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images.
Abstract: The process of aging causes significant alterations in the facial appearance of individuals When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past The methodology presented has also been used for designing a face recognition system, robust to aging variation In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved
933 citations
TL;DR: The complete state-of-the-art techniques in the face image-based age synthesis and estimation topics are surveyed, including existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are provided.
Abstract: Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
743 citations
"Comprehensive review on facial base..." refers background or methods in this paper
...In Japan, police found that a particular age group is more involved in money transfer fraud on ATMs, in which age estimation from surveillance monitoring can play an important role[2]....
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...As a principal artistic technique in forensic art, age progression is used to modify and enhance photographs by computer or manually (with professional hand drawing skills) for the purpose of suspect/victim and lost person identification with law enforcement [2][8]....
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...This technique has evolved when the photos of missing family members (especially children [2],or wanted fugitives are outdated, forensic artists can predict the natural aging of the subject faces and produce updated face images....
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...However, with the help of a computer-based automatic age estimation system, a camera snapping photos of customers could collect demographic data by capturing customers’ face images and automatically labeling age groups[2]....
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...Four concepts about human age are introduced in [2]....
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TL;DR: The age manifold learning scheme for extracting face aging features is introduced and a locally adjusted robust regressor for learning and prediction of human ages is designed, which improves the age estimation accuracy significantly over all previous methods.
Abstract: Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.
661 citations
"Comprehensive review on facial base..." refers methods in this paper
...Age manifold-- A non-personalized approach was developed by the Guo et al. [15]....
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...applied SVM to age estimation[15] , [17] on a large YGA database with 8,000 images....
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...Guo et al. [15],[17][19] applied The Support Vector Regression (SVR) method....
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...[15],[17][19] applied The Support Vector Regression (SVR) method....
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...Guo et al. applied SVM to age estimation[15] , [17] on a large YGA database with 8,000 images....
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