Abstract
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.
Highlights
Face recognition is one of the most widely used systems in the field of security [1], [2]
The results showed that the proposed Fast-Local Laplacian (FLL)-Completed Local Ternary Count (CLTC) outperformed the Completed Local Binary Pattern (CLBP) and Completed Local Ternary Pattern (CLTP) in (1,8), (2,16) and (3,24) texture patterns under a different number of training images
The results show that the proposed FLL-CLTC outperformed the CLBP and CLTP texture descriptors
Summary
Face recognition is one of the most widely used systems in the field of security [1], [2]. Different types of feature descriptors have been presented for use in the feature extraction process These features showed a good performance as powerful features and can be used to differentiate between different facial images. Some of the texture descriptors that have been proposed, such as CLBP and CLBC, have inherited the LBP drawbacks especially its noise sensitivity. A new texture descriptor, namely, Completed Local Ternary Count (CLTC) is proposed by adding a threshold to the CLBC during the extraction process. This threshold will be selected empirically and will depend on the nature of the selected database. The proposed Fast-Local Laplacian CLTC (FLL-CLTC) is evaluated for face recognition using different face image datasets.
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