A review of age estimation research to evaluate its inclusion in automated child pornography detection

The uses of artificial intelligence (AI) today seem limitless. It has helped organisations understand their customers more, provide them with better, more tailored services, and helped people with disabilities understand the world they previously could not. There are also many areas of current research for the use of AI. Aiding law-enforcement when they must analyse evidence of an indecent nature is one example where the use of AI, if successful, could enhance detection of indecent images and also reduce the workload and stress on the law enforcement staff employed in such activities. Working with indecent images of minors is particularly stressful. This paper reviews the current stage at which artificial intelligence finds itself when estimating a person’s age. By reviewing its accuracy, it is possible to evaluate the feasibility of its inclusion in an artificial-intelligence-aided evidence analysis tool. With artificial intelligence currently capable of estimating a person’s age to within a few years, its incorporation would most certainly allow photographs to be analysed and flagged if anyone is suspected of being underage

A New Cost Function Combining Deep Neural Networks (DNNs) and l2,1-Norm with Extraction of Robust Facial and Superpixels Features in Age Estimation

Automatic age estimation from unconstrained facial images is a challenging task and it recently has gained much attention due to its wide range of applications. In this paper, we propose a new model based on convolutional neural networks (CNNs) and l2,1-norm to select age-related features for the age estimation task. A new cost function is proposed. To learn and train the new model, we provide the analysis and the proof for the convergence of the new cost function to solve minimization problem of deep neural networks (DNNs) and the l2,1-norm. High-level features are extracted from the facial images by using transfer learning, since there are currently not enough large age databases that can be used to train a deep learning network. Then, the extracted features are fed to the proposed model to select the most efficient age-related features. In addition, a new system that is based on DNN to jointly fine-tune two different DNNs with two different feature sets is developed. Experimental results show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database.http://dx.doi.org/10.3390/app810194

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