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3D-2D deep convolutional neural network (DCNN) Cascade for robust video face identification

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Abstract

This paper proposes a novel video face identification method, named “3D-2D-DCNN cascade” that serially combines 3D and 2D deep convolutional neural networks (DCNNs) for robust video face recognition (FR). In our method, an input video (face) sequence is first divided into a number of sub-video sequences and each of the sub-video sequences is then used as an input to the 3D-DCNN, aiming to obtain a set of class-confidence scores for a given input video sequence. These class-confidence scores are aggregated in a novel way, resulting in the formation of our novel class-confidence matrix. Key characteristic of our method is to make use of this class-confidence matrix for fine-tuning 2D-DCNN, which is serially linked to 3D-DCNN, to obtain the final face identification results. To verify the proposed method, two popular video identification benchmarks, COX Face and YTC databases, were used. Compared to the best reported recognition results on these two benchmarks, our proposed method achieves better or comparable recognition performances.

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Funding

This research was supported by Hankuk University of Foreign Studies Research Fund. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2018R1D1A1A09082615.

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Authors and Affiliations

  1. Pattern Recognition and Machine Intelligence Laboratory, Division of Computer & Electronic Systems Engineering, Hankuk University of Foreign Studies, 81, Oedae-ro, Mohyeon-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do, 17305, Republic of Korea

    Kyeong Tae Kim & Jae Young Choi

  2. Department of Information and Communications Engineering, Chosun University, 61452, Gwangju, Republic of Korea

    Bumshik Lee

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  1. Kyeong Tae Kim
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  2. Bumshik Lee
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  3. Jae Young Choi
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Correspondence to Jae Young Choi.

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Kim, K.T., Lee, B. & Choi, J.Y. 3D-2D deep convolutional neural network (DCNN) Cascade for robust video face identification. Multimed Tools Appl 80, 4023–4036 (2021). https://doi.org/10.1007/s11042-020-09495-0

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  • DOI: https://doi.org/10.1007/s11042-020-09495-0

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