Abstract
In the past few years, deep learning-based models have been very successful in achieving state-of-the-art results in many tasks in computer vision, speech recognition, and natural language processing. These models seem to be a natural fit for handling the ever-increasing scale of biometric recognition problems, from cellphone authentication to airport security systems. Deep learning-based models have increasingly been leveraged to improve the accuracy of different biometric recognition systems in recent years. In this work, we provide a comprehensive survey of more than 150 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which deploy deep learning models, and show their strengths and potentials in different applications. For each biometric, we first introduce the available datasets that are widely used in the literature and their characteristics. We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. We will also discuss some of the main challenges while using these models for biometric recognition, and possible future directions to which research in this area is headed.
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In this paper, we commonly refer to a biometric characteristic as biometric for short.
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Acknowledgements
We would like to thank Prof. Rama Chellappa, and Dr. Nalini Ratha for reviewing this work, and providing very helpful comments and suggestions.
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Minaee, S., Abdolrashidi, A., Su, H. et al. Biometrics recognition using deep learning: a survey. Artif Intell Rev 56, 8647–8695 (2023). https://doi.org/10.1007/s10462-022-10237-x
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DOI: https://doi.org/10.1007/s10462-022-10237-x