Face recognition using a permutation coding neural classifier. Issue 4 (May 2016)

• Record Type: Journal Article
• Authors: Baidyk, T.; Kussul, E.; Cruz Monterrosas, Z.; Ibarra Gallardo, A.; Roldán Serrato, K.; Conde, C.; Serrano, A.; Martín de Diego, I.; Cabello, E.
• Is Part Of: Neural computing & applications. Volume 27:Issue 4(2016)
• Page Start: 973
• Page End: 987
• Publication Date: 2016-05
• Subjects: Permutation coding neural classifier (PCNC) -- Face recognition -- Support vector machine (SVM) -- FRAV3D face database -- Feature encoder -- Cumulative match curve (CMC) -- Distortions; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1913-0
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Face recognition is an important security task. We propose a high-level method to solve this problem: a permutation coding neural classifier (PCNC). A PCNC with a special feature extractor for face image recognition systems is a relatively new method that has been tested with good results to classify real environment images (such as larvae of various types and handmade elements). As baseline methods, a support vector machine (SVM) and the iterative closest point (ICP) method are selected for comparison. We applied these methods to gray-level images from the FRAV3D face database. Fifteen experiments were performed to examine a large set of training and testing conditions. As a general result, it was observed that errors are lower with the PCNC than with the SVM and the ICP classifier. We aggregated various distortions for the initial images to improve the PCNC. We analyze and discuss the obtained results.