Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances

Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.

Pathway analysis for genome-wide genetic variation data:Analytic principles,latest developments,and new opportunities

Pathway analysis,also known as gene-set enrichment analysis,is a multilocus analytic strategy that integrates a priori,biological knowledge into the statistical analysis of high-throughput genetics data.Originally developed for the studies of gene expression data,it has become a powerful analytic procedure for indepth mining of genome-wide genetic variation data.Astonishing discoveries were made in the past years,uncovering genes and biological mechanisms underlying common and complex disorders.However,as massive amounts of diverse functional genomics data accrue,there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data.In this review,we provide an intellectual foundation of this powerful analytic strategy,as well as an update of the state-of-the-art in recent method developments.The goal of this review is threefold:(1)introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data;(2)review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools;and(3)discuss remaining challenges and future directions for further method developments.

Approaching lean product development using system dynamics:investigating front-load effects

Competing with successful products has become perplexing with several uncertainties and transmutes from time to time as customers' expectations are dynamic.That is why manufacturing firms exhaustively strive to look for a better competitive frontier using wellestablished and innovative product development(PD)processes.In this paper,we would like to answer three research questions:(i)What would be the effects of frontloading in PD?(ii)Can we improve our PD process endlessly?(iii)When is the critical time that the firm should take remedial action for improvements?As a contribution to the vast numbers of improvement methods in new product development(NPD),this paper investigates the effects of front-loading using set-based concurrent engineering(SBCE)on cost and lead time.Models are developed and treated using a system dynamics(SD)approach.We assign a hypothetical upfront investment for SBCE and compare its effects on total cost and lead time of the development process.From the research,it is found that the total cost of PD is reduced almost by half-although the front loading is higher in order to encompass multiple design alternatives.The total product lead time is reduced by almost 20%.The model reveals the critical time for improvement of the PD process.We use SD tool(e.g.,STELLA)for simulation and visualization of the complex PD model,using SBCE as one of several strategies to frontload activities in the NPD process.

An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor performances.Hence,the authors proposed a novel model for face recognition.Design/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and recognition.Initially,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input images.The contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast level.Second,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image segmentation.Next,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture features.Finally,the support vector machine(SVM)with modified random forest(MRF)model for facial image recognition.Findings-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score results.However,the proposed method offers superior recognition performances than other state-of-art methods.Further face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,respectively.Originality/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.