The task of estimating the age of humans from facial image is a challenging one due to the non-linear and personalized pattern of aging differing from one individual to another. In this work, we investigated the problem of estimating the age of humans from their facial image using a GroupWise age ranking approach complemented by ageing pattern correlation learning. In our proposed GroupWise age-ranking approach, we constructed a reference image set grouped according to ages for each individual in the reference set and used this to obtain age-ranks for each age group in the reference set. The constructed reference set was used to obtain transformed LBP features called age-rank-biased LBP (arLBP) features which were used with attached ageranks to train an age estimating function for predicting the ages of test images. Our experiments on the publicly available FG-NET dataset and a locally collected dataset (FAGE) shows the best known age estimation accuracy with MAE of 2.34 years on FG-NET using the leave-oneperson-out strategy.
Abstract-Age is a human attribute which grows alongside an individual. Estimating human age is quite difficult for machine as well as humans, however there has been and are still ongoing efforts towards machine estimation of human age to a high level of accuracy. In a bid to improve the accuracy of age estimation from facial image, several approaches have been proposed many of which used Machine Learning algorithms. The several Machine Learning algorithms employed in these works have made significant impact on the results and of performances of the proposed age estimation approaches. In this paper, we examined and compared the performance of a number of Machine Learning algorithms used for age estimation in several previous works. Considering two publicly available facial ageing datasets (FG-NET and MORPH) which have been mostly used in previous works, we observed that Support Vector Machine (SVM) has been most popularly used and a combination/hybridization of SVM for classification (SVC) and regression (SVR) have shown the best performance so far. We also observed that the face modelling or feature extraction techniques employed significantly impacted the performance of age estimation algorithms.
Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.
The inefficiencies of current spam filters against fraudulent (419) mails is not unrelated to the use by spammers of good-word attacks, topic drifts, parasitic spamming, wrong categorization and recategorization of electronic mails by e-mail clients and of course the fuzzy factors of greed and gullibility on the part of the recipients who responds to fraudulent spam mail offers. In this paper, we establish that mail token manipulations remain, above any other tactics, the most potent tool used by Nigerian scammers to fool statistical spam filters. While hoping that the uncovering of these manipulative evidences will prove useful in future antispam research, our findings also sensitize spam filter developers on the need to inculcate within their antispam architecture robust modules that can deal with the identified camouflages.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.