16 research outputs found

    Individualised model of facial age synthesis based on constrained regression

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    YesFaces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets, unrestrained environments, ethnic and gender variations to mention but a few. This work presents a new approach to automatic facial ageing which involves the development of a person specific facial ageing system. A color based Active Appearance Model (AAM) is used to extract facial features. Then, regression is used to model an age estimator. Age synthesis is achieved by computing a solution that minimises the distance from the original face with the use of constrained regression. The model is tested on a challenging database of single image per person. Initial results suggest that plausible images can be rerendered at different ages, automatically using the AAM representation. Using the constrained regressor we are guaranteed to get estimated ages that are exact for an individual at a given age

    On automatic age estimation from facial profile view

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    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

    Comparison of filtering methods for extracting transient facial wrinkle features

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    Facial local features comprise an essential information to identify a personal characteristic such as age, gender, identity and expression. One of the facial local features is a wrinkle. Wrinkle is a small furrow or crease in the skin. Recently, wrinkle detection has become a topic of interest in computer vision, where many researchers developed applications like age estimation, face detection, expression recognition, facial digital beauty and etc. However, most of the research focused on permanent wrinkles instead of transient wrinkles. Transient wrinkle can be seen during the movement of facial muscle such as a facial expression. This paper presents a comparison of filtering method for extracting transient wrinkles features. The filters that have been selected are Gabor wavelet and Kirsch operator. The extracted features are the number of wrinkles, the maximum perimeter of wrinkle, the average perimeter of wrinkle, total perimeter of wrinkle, the maximum area of the wrinkle, and the total area of the wrinkle. A total of 60 sets of data extracted from Cohn-Kanade database, images from internet and self-images. These images contain weak and strong transient wrinkles at forehead region. Features selection and analysis has been done to select which feature extraction method produces better wrinkle features that can be used for the classification of wrinkle detection system. The results show that both Gabor and Kirsch methods are successful to extract transient wrinkle features, where both methods scored 100% accuracy in the classification with SVM. However, Gabor method is slightly better than Kirsch method in term of detecting weak wrinkles. The Kirsch method requires an additional noise filtering method to eliminate noise particles after the convolution of Kirsch’s kernel. In conclusion, Gabor method is more applicable to a variety of applications than Kirsch method

    Gender and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behaviour

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    In this paper, we introduce an approach to classify gender and age from images of human faces which is an essential part of our method for autonomous detection of anomalous human behaviour. Human behaviour is often uncertain, and sometimes it is affected by emotion or environment. Automatic detection can help to recognise human behaviour which later can assist in investigating suspicious events. Central to our proposed approach is the recently introduced transfer learning. It was used on the basis of deep learning and successfully applied to image classification area. This paper is a continuous study from previous research on heterogeneous data in which we use images as supporting evidence. We present a method for image classification based on a pre-trained deep model for feature extraction and representation followed by a Support Vector Machine classifier. Because very few data sets with labels of gender and age exist of face images, we build one dataset named GAFace and applied our proposed method to this dataset achieving excellent results and robustness (gender classification: 90.33% and age classification: 80.17% accuracy) approaching human performance

    Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression

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    Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation

    Expression-Invariant Age Estimation

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