Automatic Age Estimation From Real-World And Wild Face Images By Using Deep Neural Networks

Automatic age estimation from real-world and wild face images is a challenging task and has an increasing importance due to its wide range of applications in current and future lifestyles. As a result of increasing age specific human-computer interactions, it is expected that computerized systems should be capable of estimating the age from face images and respond accordingly. Over the past decade, many research studies have been conducted on automatic age estimation from face images. In this research, new approaches for enhancing age classification of a person from face images based on deep neural networks (DNNs) are proposed. The work shows that pre-trained CNNs which were trained on large benchmarks for different purposes can be retrained and fine-tuned for age estimation from unconstrained face images. Furthermore, an algorithm to reduce the dimension of the output of the last convolutional layer in pre-trained CNNs to improve the performance is developed. Moreover, two new jointly fine-tuned DNNs frameworks are proposed. The first framework fine-tunes tow DNNs with two different feature sets based on the element-wise summation of their last hidden layer outputs. While the second framework fine-tunes two DNNs based on a new cost function. For both frameworks, each has two DNNs, the first DNN is trained by using facial appearance features that are extracted by a well-trained model on face recognition, while the second DNN is trained on features that are based on the superpixels depth and their relationships. Furthermore, a new method for selecting robust features based on the power of DNN and ??21-norm is proposed. This method is mainly based on a new cost function relating the DNN and the L21 norm in one unified framework. To learn and train this unified framework, the analysis and the proof for the convergence of the new objective function to solve minimization problem are studied. Finally, the performance of the proposed jointly fine-tuned networks and the proposed robust features are used to improve the age estimation from the facial images. The facial features concatenated with their corresponding robust features are fed to the first part of both networks and the superpixels features concatenated with their robust features are fed to the second part of the network. Experimental results on a public database show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database

Design sparse features for age estimation using hierarchical face model

A key point in automatic age estimation is to design feature set essential to age perception. To achieve this goal, this paper builds up a hierarchical graphical face model for faces appearing at low, middle and high resolution respectively. Along the hierarchy, a face image is decomposed into detailed parts from coarse to fine. Then four types of features are extracted from this graph representation guided by the priors of aging process embedded in the graphical model: topology, geometry, photometry and configuration. On age estimation, this paper follows the popular regression formulation for mapping feature vectors to its age label. The effectiveness of the presented feature set is justified by testing results on two datasets using different kinds of regression methods. The experimental results in this paper show that designing feature set for age estimation under the guidance of hierarchical face model is a promising method and a flexible framework as well. 1

Automatic facial age estimation

The reliability of automatically estimating human ages, by processing input facial images, has generally been found to be poor. On other hand, various real world applications, often relating to safety and security, depend on an accurate estimate of a person’s age. In such situations, Face Image based Automatic Age Estimation (FI-AAE) systems which are more reliable and may ideally surpass human ability, are of importance as and represent a critical pre-requisite technology. Unfortunately, in terms of estimation accuracy and thus performance, contemporary FI-AAE systems are impeded by challenges which exist in both of the two major FI-AAE processing phases i.e. i) Age based feature extraction and representation and ii) Age group classification. Challenges in the former phase arise because facial shape and texture change independently and the magnitude of these changes vary during the different stages of a person’s life. Additionally, contemporary schemes struggle to exploit age group specific characteristics of these features, which in turn has a detrimental effect on overall system performance. Furthermore misclassification errors which occur in the second processing phase and are caused by the smooth inter-class variations often observed between adjacent age groups, pose another major challenge and are responsible for low overall FI-AAE performance. In this thesis a novel Multi-Level Age Estimation (ML-AE) framework is proposed that addresses the aforementioned challenges and improves upon state-of-the-art FI-AAE system performance. The proposed ML-AE is a hierarchical classification scheme that maximizes and then exploits inter-class variation among different age groups at each level of the hierarchy. Furthermore, the proposed scheme exploits age based discriminating information taken from two different cues (i.e. facial shape and texture) at the decision level which improves age estimation results. During the process of achieving our main objective of age estimation, this research work also contributes to two associated image processing/analysis areas: i) Face image modeling and synthesis; a process of representing face image data with a low dimensionality set of parameters. This is considered as precursor to every face image based age estimation system and has been studied in this thesis within the context of image face recognition ii) measuring face image data variability that can help in representing/ranking different face image datasets according to their classification difficulty level. Thus a variability measure is proposed that can also be used to predict the classification performance of a given face recognition system operating upon a particular input face dataset. Experimental results based on well-known face image datasets revealed the superior performance of our proposed face analysis, synthesis and face image based age classification methodologies, as compared to that obtained from conventional schemes

Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka

Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka