Browsing SoC Research Publications by Author "Rattani, Ajita"

Now showing items 1-15 of 15

    • Adaptive scaffolding toward transdisciplinary collaboration: Reflective polyvocal self-study 

      Alagic, Mara; Sclafani, Maria; Filbert, Nathan; Rimmington, Glyn; Demissie, Zelalem S.; Dutta, Atri; Bowen, Aaron; Lindsay, Ethan; Kuhlmann, Meghann; Rattani, Ajita; Rai, Atul (Springer International Publishing, 2022-12-16)
      Contemporary global challenges require experts from various disciplines to work together. Since every field of knowledge has its unique language and discipline-based culture, collaborative inquiry presents an additional ...

    • An examination of bias of facial analysis based BMI prediction models 

      Siddiqui, Hera; Rattani, Ajita; Ricanek, Karl; Hill, Twyla (IEEE, 2022-06-19)
      Obesity is one of the most important public health problems that the world is facing today. A recent trend is in the development of intervention tools that predict BMI using facial images for weight monitoring and management ...

    • An LDOP approach for face identification under unconstrained scenarios 

      Datta Rakshit, Rinku; Rattani, Ajita; Kisku, Dakshina Ranjan (Taylor & Francis, 2023-03-01)
      In unconstrained environments, it encounters a number of challenges when considering handcrafted features for face recognition, including changes in pose, illumination and facial expression and plastic surgery variations, ...

    • Benchmarking neural network compression techniques for ocular-based user authentication on smartphones 

      Almadan, Ali; Rattani, Ajita (Institute of Electrical and Electronics Engineers Inc., 2023-04-06)
      With the unprecedented mobile technology revolution, mobile devices have transcended from being the primary means of communication to an all-in-one platform. Consequently, an increasing number of individuals are accessing ...

    • Compact CNN models for on-device ocular-based user recognition in mobile devices 

      Almadan, Ali; Rattani, Ajita (IEEE, 2021-12-05)
      A number of studies have demonstrated the efficacy of deep learning convolutional neural network (CNN) models for ocular-based user recognition in mobile devices. However, these high-performing networks have enormous space ...

    • An experimental evaluation on deepfake detection using deep face recognition 

      Ramachandran, Sreeraj; Nadimpalli, Aakash Varma; Rattani, Ajita (IEEE, 2021-10-11)
      Significant advances in deep learning have obtained hallmark accuracy rates for various computer vision applications. However, advances in deep generative models have also led to the generation of very realistic fake ...

    • Harnessing unlabeled data to improve generalization of biometric gender and age classifiers 

      Nadimpalli, Aakash Varma; Reddy, Narsi; Ramachandran, Sreeraj; Rattani, Ajita (IEEE, 2021-12-05)
      With significant advances in deep learning, many computer vision applications have reached the inflection point. However, these deep learning models need large amount of labeled data for model training and optimum parameter ...

    • Investigating fairness of ocular biometrics among young, middle-aged, and older adults 

      Krishnan, Anoop; Almadan, Ali; Rattani, Ajita (IEEE, 2021-10-11)
      A number of studies suggest bias of the face biometrics, i.e., face recognition and soft-biometric estimation methods, across gender, race, and age-groups. There is a recent urge to investigate the bias of different biometric ...

    • Is Facial Recognition Biased at Near-Infrared Spectrum as Well? 

      Krishnan, Anoop; Neas, Brian; Rattani, Ajita (IEEE, 2022-11-15)
      Published academic research and media articles suggest face recognition is biased across demographics. Specifically, unequal performance is obtained for women, dark-skinned people, and older adults. However, these published ...

    • Multimodal combination of text and image tweets for disaster response assessment 

      Kotha, Saideshwar; Haridasan, Smitha; Rattani, Ajita; Bowen, Aaron; Rimmington, Glyn M.; Dutta, Atri (CEUR-WS, 2022-07-06)
      Social media platforms are a vital source of information in times of natural and man-made disasters. People use social media to report updates about injured or dead people, infrastructure damage, missing or found people ...

    • Multispectral deep learning models for wildfire detection 

      Haridasan, Smitha; Rattani, Ajita; Demissie, Zelalem; Dutta, Atri (CEUR-WS, 2022-07-06)
      Aided by wind, all it takes is one ember and few minutes to create wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. ...

    • On improving cross-dataset generalization of deepfake detectors 

      Nadimpalli, Aakash Varma; Rattani, Ajita (IEEE, 2022-06-19)
      Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake ...

    • Selfie Biometrics : Advances and Challenges 

      Rattani, Ajita; Derakhshani, Reza; Ross, Arun A. (Springer, 2019)
      This book highlights the field of selfie biometrics, providing a clear overview and presenting recent advances and challenges. It also discusses numerous selfie authentication techniques on mobile devices. Biometric ...

    • A survey on machine and deep learning models for childhood and adolescent obesity 

      Siddiqui, Hera; Rattani, Ajita; Woods, Nikki Keene; Cure, Laila; Lewis, Rhonda K.; Twomey, Janet M.; Smith-Campbell, Betty; Hill, Twyla J. (IEEE, 2021-11-25)
      Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now ...

    • Toward on-device weight monitoring from selfie face images using smartphones 

      Siddiqui, Hera; Rattani, Ajita; Cure, Laila; Woods, Nikki Keene; Lewis, Rhonda K.; Twomey, Janet M.; Smith-Campbell, Betty; Hill, Twyla J. (Springer, 2022-02-24)
      Obesity is a serious health problem that is on the rise at the global level. Recent studies suggest that BMI can be inferred from facial images using deep learning-based convolutional neural networks (CNNs) for obesity ...