A Novel Feature Extraction Descriptor for Face Recognition
Received: 15 November 2021 | Revised: 29 November 2021 | Accepted: 4 December 2021 | Online: 12 February 2022
Corresponding author: A. B. S. Salamh
Abstract
This paper presents a new feature extraction technique for face recognition. The new model, called multi-descriptor, is based on the well-known method of local binary patterns. It involves many different neighborhoods of the central pixel. Its unique advantage is that this descriptor allows the use of different neighborhood sizes instead of only one point. This structure ensures reasonable effectiveness and also provides the possibility to obtain a different distribution of features. Based on the new descriptor, a face recognition model using the pairwise feature descriptor based on the proposed descriptor was developed in this work, and local binary patterns were created to investigate the similarity and dissimilarity between the two models. For both models, the training was done using the support vector machine method on different face databases to overcome face recognition problems such as camera distance, expression, large head size, and illumination variations. The proposed technique achieved perfect accuracy on almost all tested databases including the Extended Yale B and Grimace database.
Keywords:
multi descriptor model, local binary pattern, face recognition, feature extractionDownloads
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