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.

/pdf/age-synthesis-and-estimation-via-faces-a-survey-4mmta1vo62.pdf

Age Synthesis and Estimation via Faces: A Survey

Texture features for image classification and retrieval.

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

/pdf/big-data-analytics-in-healthcare-161n26polf.pdf

Big Data Analytics in Healthcare

Age estimation using a hierarchical classifier based on global and local facial features

Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel Convolutional Neural Network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a series of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. We theoretically obtain a much tighter error bound for ranking-based age estimation. Moreover, we rigorously prove that ranking-CNN is more likely to get smaller estimation errors when compared with multi-class classification approaches. Through extensive experiments, we show that statistically, ranking-CNN significantly outperforms other state-of-the-art age estimation models on benchmark datasets.

https://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Using_Ranking-CNN_for_CVPR_2017_paper.pdf

Using Ranking-CNN for Age Estimation

Estimating the age exactly and then producing the younger and older images of the person is important in security systems design. In this paper local binary patterns are used to classify the age from facial images. The local binary patterns (LBP) are fundamental properties of local image texture and the occurrence histogram of these patterns is an effective texture feature for face description. In the study we classify the FERET images according to their ages with 10 years intervals. The faces are divided into small regions from which the LBP histograms are extracted and concatenated into a feature vector to be used as an efficient face descriptor. For every new face presented to the system, spatial LBP histograms are produced and used to classify the image into one of the age classes. In the classification phase, minimum distance, nearest neighbor and k-nearest neighbor classifiers are used. The experimental results have shown that system performance is 80% for age estimation.

Automatic age classification with LBP

http://lms.ctl.cyut.edu.tw/sysdata/8/21108/doc/d7b84166985e286b/attach/905835.pdf

Medical image security and EPR hiding using Shamir's secret sharing scheme

Secret image sharing scheme with adaptive authentication strength

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

Ensuring authenticity of images has become an important issue recently. Copy move forgery is one of the most common tampering techniques used to modify images. Copy move forgery detection techniques in the literature divide the image into overlapping blocks and use various techniques to extract features from the blocks. Similarity between the feature vectors is a clue about the forgery. However, these techniques use a predefined threshold to test the similarity. Test images with different characteristics require various threshold values. Determination of the best threshold value can be troublesome because the range of the feature vector elements’ cannot be predetermined. Therefore, many experiments must be realized to find the best threshold value. In this work, we utilize DCT-phase terms to restrict the range of the feature vector elements’ and Benford’s generalized law to determine the compression history of the image under test. The method uses element-by-element equality between the feature vectors instead of Euclidean distance or cross correlation and utilizes compression history to determine the threshold value for the current test image automatically. Experimental results show that the method can detect the copied and pasted regions under different scenarios and gives higher accuracy ratios/lower false negative compared to similar works.

Vasif V. Nabiyev

Papers

Automatic age classification with LBP

Medical image security and EPR hiding using Shamir's secret sharing scheme

Secret image sharing scheme with adaptive authentication strength

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

A new copy move forgery detection technique with automatic threshold determination