Open AccessFace Recognition System Using Local Features Fusion for Multi-Masks
Author(s) -
Mustafa Zuhaer Nayef Al-Dabagh,
Muhammad Ahmad
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2107/1/012044
Subject(s) - computer science , artificial intelligence , facial recognition system , face (sociological concept) , pattern recognition (psychology) , feature extraction , feature (linguistics) , three dimensional face recognition , field (mathematics) , computer vision , face detection , mathematics , social science , linguistics , philosophy , sociology , pure mathematics
Face recognition is a relatively novel research field, and its application is closely related to numerous other areas. Moreover, it is emerging as a critical research theme due to its broad range of applications. Thus, many face recognition methods use a variety of feature extraction approaches. Nonetheless, the issue continues to be challenging, particularly identifying non-biological entities. This paper proposes an extended descriptor for local features of an effectual facial recognition system using a local directional pattern operator. This technique combines the Frei-Chen and Robinson masks’ strengths by fusion of the directional features of LDP for these two masks; this elicits a robust feature extraction method for distinguishing faces. Experimental results using the Yale database show that the extended descriptor considerably improved recognition rate and better performance than traditional local feature descriptors.