Face recognition using a permutation coding neural classifier

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.

Analysis of Random Local Descriptors in Face Recognition

“…Our approach is based on the addition of rotation distortions to the training set. The results improved from 46.6% to 23.00% for four distortions, from 41.7% to 21.00% for eight distortions and from 31.1% to 16.00% for 12 distortions [35,36]. In comparison with the basic version (without rotations), the new version significantly improved the recognition rate by decreasing by approximately twice the number of errors.…”

“…We consider the middle of the face image to be the origin O(w/2,h/2). For our experiments, we selected three values of the clockwise rotation angle (the reference point is the vertical axis) θ = 5 • , 10 • , 15 • , and three values for counterclockwise rotations θ = −5 • , −10 • , −15 • [35,36].…”

Race Recognition Using Deep Convolutional Neural Networks

“…Nowadays, surveillance systems contribute vitally to public security. The development of artificial intelligence, especially artificial intelligence for computer vision [1], has made it easier to analyze the resulting videos [2,3]. Several studies have recently addressed the problem of event detection in video surveillance [4] which requires the ability to identify and localize specified spatiotemporal patterns.…”

“…The main contributions of this work are as follows. (1) We introduce a race dataset of Vietnamese people collected from a social network and published for academic use. (2) We propose an efficient framework including three modules for information collection (IC), face detection and preprocessing (FD&P), and RR.…”