Random sampling for patch-based face recognition

Ismahane Cheheb, Noor Al-Maadeed, Somaya Al-Madeed, Ahmed Bouridane, Richard Jiang

Research output: Contribution to conferencePaper

17 Citations (Scopus)
22 Downloads (Pure)


Real face recognition is a challenging problem especially when face images are subject to distortions. This paper presents an approach to tackle partial occlusion distortions present in real face recognition using a single training sample per person. First, original images are partitioned into multiple blocks and Local Binary Patterns are applied as a local descriptor on each block separately. Then, a dimensionality reduction of the resulting descriptors is carried out using Kernel Principle Component Analysis. Once done, a random sampling method is used to select patches at random and hence build several sub-SVM classifiers. Finally, the results from each sub-classifier are combined in order to increase the recognition performance. To demonstrate the usefulness of the approach, experiments were carried on the AR Face Database and obtained results have shown the effectiveness of our technique.
Original languageEnglish
Publication statusPublished - 29 May 2017
EventIWBF 2017 - 5th International Workshop on Biometrics and Forensics - Coventry, UK
Duration: 29 May 2017 → …


WorkshopIWBF 2017 - 5th International Workshop on Biometrics and Forensics
Period29/05/17 → …


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