TY - JOUR
T1 - Biometrics recognition using deep learning
T2 - a survey
AU - Minaee, Shervin
AU - Abdolrashidi, Amirali
AU - Su, Hang
AU - Bennamoun, Mohammed
AU - Zhang, David
PY - 2023/1/13
Y1 - 2023/1/13
N2 - 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.
AB - 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.
KW - Biometric recognition
KW - Deep learning
KW - Face recognition
KW - Fingerprint recognition
KW - Iris recognition
KW - Palmprint recognition
KW - Ear recognition
KW - Voice recognition
KW - Signature recognition
KW - SUPPORT VECTOR MACHINES
KW - VIEW GAIT RECOGNITION
KW - NEURAL-NETWORK MODEL
KW - 3D FACE RECOGNITION
KW - SIGNATURE VERIFICATION
KW - PALMPRINT RECOGNITION
KW - IRIS RECOGNITION
KW - SPEAKER RECOGNITION
KW - FUSION
KW - REPRESENTATION
UR - http://www.scopus.com/inward/record.url?scp=85146223860&partnerID=8YFLogxK
U2 - 10.1007/s10462-022-10237-x
DO - 10.1007/s10462-022-10237-x
M3 - Article
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
SN - 0269-2821
ER -