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  • Open Access


    Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

    Raqinah Alrabiah, Muhammad Hussain*, Hatim A. AboAlSamh

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2941-2962, 2023, DOI:10.32604/iasc.2023.030036

    Abstract The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human–computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing gender classification approaches have been… More >

  • Open Access


    Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

    Raniyah Wazirali1, Rami Ahmed2,*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 305-320, 2022, DOI:10.32604/csse.2022.020504

    Abstract The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required… More >

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