Driver drowsiness detection is of great significance in improving driving safety and has been widely studied in recent years. However, some existing methods have not fully utilized the drowsiness-related information, and some methods are susceptible to interference from the redundant information of input data. To address these issues, a video-based driver drowsiness detection method according to the key facial features including facial landmarks and local facial areas (VBFLLFA) is proposed in this paper. In order to fully utilize the key facial features related to drowsiness and exclude the interference of redundant information, the head movement information is obtained through facial landmark analysis and the movement information of eyes and mouth is acquired from the local facial areas. And the spatial filtering based on the common spatial pattern (CSP) algorithm is introduced to improve the discrimination of different classes of samples. To adequately extract the temporal and spatial features, a two-branch multi-head attention (TB-MHA) module is designed in this paper. Furthermore, the center loss with center vector distance penalty is introduced to further improve the discrimination of different classes of samples in the feature space. In addition to two public datasets, we specifically create a novel video-based driver drowsiness detection (VBDDD) dataset to evaluate the effectiveness of our method. The experimental results verify that our method can achieve very excellent performance in driver drowsiness detection tasks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features


    Contributors:
    Yang, Lie (author) / Yang, Haohan (author) / Wei, Henglai (author) / Hu, Zhongxu (author) / Lv, Chen (author)


    Publication date :

    2024-07-01


    Size :

    4691632 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Driver Drowsiness Detection From Multiple Facial Features Using Mobile Devices

    John, Jyothish K / Jose, Jenat / Cyriac, Deepu et al. | IEEE | 2023


    Drowsiness detection using facial expression features

    Hachisuka, Satori / Kumura, Teiyuu / Ishida, Kenji et al. | Tema Archive | 2010


    Drowsiness detection using facial expression features

    Hachisuka,S. / Kimura,T. / Ishida,K. et al. | Automotive engineering | 2010


    Drowsiness Detection Using Facial Expression Features

    Nakatani, Hiroto / Ishida, Kenji / Ozaki, Noriyuki et al. | SAE Technical Papers | 2010


    Driver drowsiness detection

    YANG HSIN-HSIANG / PRAKAH-ASANTE KWAKU O | European Patent Office | 2015

    Free access