Showing papers by "Neeta Nain published in 2021"

A texture feature based approach for person verification using footprint bio-metric

Abstract: Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

Child Face Age Progression and Regression using Self-Attention Multi-Scale Patch GAN

Abstract: Face age progression and regression have accumulated significant dynamic research enthusiasm because of its gigantic effect on a wide scope of handy applications including finding lost/wanted persons, cross-age face recognition, amusement, and cosmetic studies. The two primary necessities of face age progression and regression, are identity preservation and aging exactitude. The existing state-of-the-art frameworks mostly focus on adult or long-span aging. In this work, we propose a child face age-progress and regress framework that generates photo-realistic face images with preserved identity.To facilitate child age synthesis, we apply a multi-scale patch discriminator learning strategy for training conditional generative adversarial nets (cGAN) which in-creases the stability of the discriminator, thereby making the learning task progressively more difficult for the generator. Moreover, we also introduce Self-Attention Block (SAB) to learn global and long-term dependencies within an internal representation of a child’s face. Thus, we present coarse-to-fine Self-Attention Multi-Scale Patch generative adversarial nets (SAMSP-GAN) model. Our new objective function, as well as multi-scale patch discrimination and, has shown both qualitative and quantitative improvements over the state-of-the-art approaches in terms of face verification, rank-1 identification, and age estimation on benchmarked children datasets.

Collaborative Recommender System (CRS) Using Optimized SGD - ALS

Abstract: Matrix factorization (MF), dimensional reduction techniques are broadly used in recommender systems (RS) to retrieve the preference of user from explicit ratings. However, the interactions are not always consistent due to the influence of numerous elements on users on a product, including friend’s recommendation and business publicizing. In comparison, traditional MF is not able to find consistent ratings. Find the exact prediction/ratings of a product/item is essential for further improvement of the performance of the collaborative recommender framework. To find the exact prediction, we propose the parameter optimizing stochastic gradient descent (SGD) and alternate least square (ALS) over MF. Furthermore, we examine the deviation of prediction error after setting each parameter over a general parameter distribution of both techniques (SGD and ALS). To evaluate the performance of the proposed model, we use two well-known datasets. The exploratory outcomes reveal that our approach gets significant improvement over the base model.

Longitudinal Analysis of Mask and No-Mask on Child Face Recognition.

Abstract: Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains $26,258$ face images of $7,473$ subjects in the age group of $[2, 18]$ over an average time span of $3.35$ years. Experimental results showed that problem of face mask on automated face recognition is compounded by aging variate.