A new fast local laplacian completed local ternary count (FLL-CLTC) for facial image classification

• Main Authors: Alalayah, Khaled M., Reyazur Rashid, Irshad, Rassem, Taha H., Mohammed, Badiea Abdulkarem
• Format: Article
• Language: English; English
• Published: IEEE 2020
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/28457/1/09099214.pdf; http://umpir.ump.edu.my/id/eprint/28457/7/A%20New%20Fast%20Local%20Laplacian%20Completed%20Local%20Ternary%20Count.pdf; http://umpir.ump.edu.my/id/eprint/28457/; https://doi.org/10.1109/ACCESS.2020.2997312; https://doi.org/10.1109/ACCESS.2020.2997312
• Institution: Universiti Malaysia Pahang

Face recognition is one of the most interesting areas of research areas because of its importance in authentication and security. Differentiating between different facial images is not easy because of the similarities in facial features. Human faces can also be covered obscured by eyeglasses, facial expressions and hairstyles can also be changed causing difficulty in finding similar faces. Thus, the need for powerful image features has become a critical issue in the face recognition systems. Many texture features have been used in these systems, including Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC) and Completed Local Ternary Pattern (CLTP). In this paper, a new texture descriptor, namely, Completed Local Ternary Count (CLTC), is proposed by adding a threshold value for the CLBC to overcome its sensitivity to noise drawback. The CLTC is also enhanced by adding the Fast-Local Laplacian filter during the pre-processing stage to increase the discriminative property of the proposed descriptor. The proposed Fast-Local Laplacian CLTC (FLL-CLTC) texture descriptor is evaluated for face recognition task using five different face image datasets. The experimental results of the FLL-CLTC showed that the proposed FLL-CLTC outperformed the CLBP and CLTP texture descriptors in term of recognition accuracy. The FLL-CLTC achieved 99.1%, 86.93%, 93.21%, 84.92% and 99.15% with JAFFE, YALE, Georgia Tech, Caltech and ORL face image datasets, respectively.