An up-to-date, comprehensive and compact overview of the vast amount of work on image and video based face recognition in the literature.
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A novel taxonomy of image and video-based methods, which also contains recent methods such as sparsity and deep learning based methods.
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An up-to-date review of the image and video-based data sets used for face recognition.
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Review of the recent deep-learning based methods, which have shown remarkable results on large scale and unconstrained challenging data sets.
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Information on both image and video-based methods, with an emphasis on the video-based methods.
Biometric systems have the goal of measuring and analyzing the unique physical or behavioral characteristics of an individual. The main feature of biometric systems is the use of bodily structures with distinctive characteristics. In the literature, there are biometric systems that use physiological features (fingerprint, iris, palm print, face, etc.) as well as systems that use behavioral characteristics (signature, walking, speech patterns, facial dynamics, etc.) Recently, facial biometrics has been one of the most preferred biometric data since it generally does not require the cooperation of the user and can be obtained without violating the personal private space. In this paper, the methods used to obtain and classify facial biometric data in the literature have been summarized. We give a taxonomy of image-based and video-based face recognition methods, outline the major historical developments, and the main processing steps. Popular data sets that have been used for face recognition by researchers are also reviewed. We also cover the recent deep-learning based methods for face recognition and point out possible directions for future research.
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Murat Taskiran received B.Sc. (2013) and M.Sc. (2016) degrees in Electronics and Communication Engineering, from Yildiz Technical University (YTU), Istanbul, Turkey. He is currently a Ph.D. student at YTU. Since 2014, he has been working as a research assistant in Department of Electronics and Communication Engineering in YTU. His research interests are in image processing, neural networks and randomness analysis.
Nihan Kahraman received B.Sc. (2001), M.Sc. (2003) and Ph.D. (2008) degrees in Electronics and Communication Engineering, from Yildiz Technical University (YTU), Istanbul, Turkey. Currently, she is working as an assistant professor at YTU. Her research interests are includes VLSI design, hardware and software implementations of neural networks and neural network architectures.
Cigdem Eroglu Erdem received the B.S. and M.Sc. degrees in Electrical and Electronics Engineering from Bilkent University, Ankara, Turkey in 1995 and 1997, respectively, with high honors. She received the Ph.D. degree in Electrical and Electronics Engineering from Bogazici University, Istanbul, Turkey, in 2002. From September 2000 to June 2001, she was a visiting researcher in the Department of Electrical and Computer Engineering, University of Rochester, NY, USA. Between 2003-2004, she was a postdoctoral fellow at the Faculty of Electrical Engineering at Delft University of Technology, the Netherlands, where she was also affiliated with the video processing group at Philips Research Laboratories, Eindhoven. Between 2002-2009, she was the director of research at Momentum Digital Media Technologies Inc., a technology SME located in İstanbul. Between 2009-2016, she was a faculty member in the Department of Electrical and Electronics Engineering at Bahcesehir University, Istanbul, Turkey. Since Sep. 2016, she is a faculty member in the Department of Computer Engineering at Marmara University, Istanbul, Turkey.
Dr. Erdem's current research interests are in the areas of digital image and video processing, computer vision and pattern recognition, with applications to affective computing, motion estimation, video segmentation, object tracking, and human computer interaction. She served as a referee for numerous technical journals and conferences including IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition, Signal Processing: Image Communication, Image and Vision Computing. She also serves as an independent expert and vice chair during project evaluations for the European Commission.