Faceless identification based on temporal strips

Shu Min Leong, Raphaël C.W. Phan, Vishnu Monn Baskaran, Chee Pun Ooi

    Research output: Contribution to journalArticleResearchpeer-review


    This paper first presents a novel approach for modelling facial features, Local Directional Texture (LDT), which exploits the unique directional information in image textures for the problem of face recognition. A variant of LDT with privacy-preserving temporal strips (TS) is then considered to achieve faceless recognition with a higher degree of privacy while maintaining high accuracy. The TS uses two strips of pixel blocks from the temporal planes, XT and YT, for face recognition. By removing the reliance on spatial context (i.e., XY plane) for this task, the proposed method withholds facial appearance information from public view, where only one-dimensional temporal information that varies across time are extracted for recognition. Thus, privacy is assured, yet without impeding the facial recognition task which is vital for many security applications such as street surveillance and perimeter access control. To validate the reliability of the proposed method, experiments were carried out using the Honda/UCSD, CK+, CAS(ME)2 and CASME II databases. The proposed method achieved a recognition rate of 98.26% in the standard video-based face recognition database, Honda/UCSD. It also offers a 81.92% reduction in the dimension length required for storing the extracted features, in contrast to the conventional LBP-TOP.

    Original languageEnglish
    Pages (from-to)279-298
    Number of pages20
    JournalMultimedia Tools and Applications
    Issue number1
    Publication statusPublished - Jan 2021


    • Facial recognition
    • Local binary pattern (LBP)
    • Local directional textures
    • Partial facial features
    • Privacy-preserving
    • Temporal features

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