Abstract
This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition.
Highlights
Face recognition offers the advantage of being a passive identification and verification method that does not require explicit action or participation by the individual in order to be recognized
The results showed that the Permutation Coding Neural Classifier (PCNC) neural classifier and the Support Vector Machine (SVM) [26] method suffered from the same recognition problems, i.e., rotations
The PCNC neural classifier is based on the Random Local Descriptor (RLD) descriptors that are used for feature extraction from the image
Summary
Face recognition offers the advantage of being a passive identification and verification method that does not require explicit action or participation by the individual in order to be recognized. Use LBP for face subregions recognition,are it isnot necessary to divide face images a grid of the resulting facial description depends on the chosen sizes and positions subregions These subregions are not necessarily well aligned with facial features.of these subregions [15]. LBP-based features have a large dimensionality; to reduce this, this method is combined with some popular learning techniques which were developed and used for texture recognition, and for face recognition tasks. We describe all of these local descriptors in detail to demonstrate the interest of scientists and engineers in the image recognition area, and in order to have examples with which to compare the advantages and disadvantages of these methods with the methods that we are proposing.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
