Computational Intelligence in Automatic Face Age Estimation: A Survey

With the rapid growth of computational intelligence techniques, automatic face age estimation has achieved good accuracy that benefited real-world applications such as access control and monitoring, soft biometrics, and information retrieval. Over the past decade, many new algorithms were developed and previous surveys on face age estimation were either outdated or incomplete. Considering the importance of the expanding research in this topic, we aim to provide an up-to-date survey on the face age estimation techniques. First, we summarize the state-of-the-art databases and the performance metrics for face age estimation. Then, we review the age estimation techniques based on three categories of face features (local, global, and hybrid) and discuss different types of age learning algorithms. Finally, we identify the challenges and provide new insights for future research directions of fully automated face age estimation

Automatic real and apparent age estimation in still images

We performed a study on age estimation via still images creating a new face image database containing real age and apparent age label annotations. Two age estimation methods are proposed using the state of the art techniques and analyse their performance with the proposed database

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

On automatic age estimation from facial profile view

YesIn recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side-view of face images. Here we exploit this by using a pre-trained deep residual neural network (ResNet) to extract features. We then utilize a sparse partial least squares regression approach to estimate ages. Despite having less information as compared to frontal images, our results show that the extracted deep features achieve a promising performance

Hierarchical age estimation using enhanced facial features.

Doctor of Philosopy in Computer Science, University of KwaZulu-Natal, Westville, 2018.Ageing is a stochastic, inevitable and uncontrollable process that constantly affect shape, texture and general appearance of the human face. Humans can easily determine ones’ gender, identity and ethnicity with highest accuracy as compared to age. This makes development of automatic age estimation techniques that surpass human performance an attractive yet challenging task. Automatic age estimation requires extraction of robust and reliable age discriminative features. Local binary patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age discriminative features. Although local ternary patterns (LTP) is insensitive to noise, it uses a single static threshold for all images regardless of varied image conditions. Local directional patterns (LDP) uses k directional responses to encode image gradient and disregards not only central pixel in the local neighborhood but also 8 k directional responses. Every pixel in an image carry subtle information. Discarding 8 k directional responses lead to lose of discriminative texture features. This study proposes two variations of LDP operator for texture extraction. Significantorientation response LDP (SOR-LDP) encodes image gradient by grouping eight directional responses into four pairs. Each pair represents orientation of an edge with respect to central reference pixel. Values in each pair are compared and the bit corresponding to the maximum value in the pair is set to 1 while the other is set to 0. The resultant binary code is converted to decimal and assigned to the central pixel as its’ SOR-LDP code. Texture features are contained in the histogram of SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the difference between neighboring pixels and central pixel in 3 3 image region. These differential values are convolved with Kirsch edge detectors to obtain directional responses. These responses are normalized and used as probability of an edge occurring towards a respective direction. An adaptive threshold is applied to derive LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms of negative and positive LTDP encoded images are concatenated to obtain texture feature. Regardless of there being evidence of spatial frequency processing in primary visual cortex, biologically inspired features (BIF) that model visual cortex uses only scale and orientation selectivity in feature extraction. Furthermore, these BIF are extracted using holistic (global) pooling across scale and orientations leading to lose of substantive information. This study proposes multi-frequency BIF (MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol, this study investigated performance of proposed feature extractors in age estimation in a hierarchical way by performing age-group classification using Multi-layer Perceptron (MLP) followed by within age-group exact age regression using support vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were used to evaluate performance of proposed face descriptors. Experimental results on FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform state-of-the-art feature descriptors in age estimation. Experimental results show that performing gender discrimination before age-group and age estimation further improves age estimation accuracies. Shape, appearance, wrinkle and texture features are simultaneously extracted by visual system in primates for the brain to process and understand an image or a scene. However, age estimation systems in the literature use a single feature for age estimation. A single feature is not sufficient enough to capture subtle age discriminative traits due to stochastic and personalized nature of ageing. This study propose fusion of different facial features to enhance their discriminative power. Experimental results show that fusing shape, texture, wrinkle and appearance result into robust age discriminative features that achieve lower MAE compared to single feature performance