مقاله انگلیسی رایگان در مورد تشخیص چهره تحت تغییر عبارات و نورپردازی – الزویر 2018

Social networks generate enormous amounts of visual data. Mining of such data in recommender systems is extremely important. User profiling is carried out in recommender systems to build the holistic persona of the user. Identification and grouping of images in these systems is carried out using face recognition. It is one of the most appropriate biometric features in such situations. Ever since the first use of face recognition in security and surveillance systems, researchers have developed many methods with improved accuracy. Face recognition under variant illumination is still an open issue and diverging facial expressions reduces the accuracy even further. State of the art methods produced an average accuracy of 90%.In this study, a computationally intelligent and efficient method based on particle swarm optimization (PSO) is developed. It utilizes the features extracted from texture and wavelet domain. Discrete Wavelet Transform provides the advantage of extracting relevant features and thereby reducing computational time and an increase in recognition accuracy rate. We apply particle swarm optimization technique to select informative wavelet sub-band. Furthermore, the proposed technique uses Discrete Fourier Transform to compensate the translational variance problem of the discrete wavelet transform. The proposed method has been tested on the CK, MMI and JAFFE databases. Experimental results are compared with existing techniques and the results indicate that the proposed technique is more robust to illumination and variation in expressions, average accuracy obtained over the CK, MMI and JAFFE datasets is 98.6%, 95.5%, and 98.8% respectively.

Introduction

Face Recognition (FR) is a biometric solution that is used for the identification or authentication of a human from a video or image source. It has been successfully utilized in a variety of domains. Key application areas of facial recognition include augmented reality, retailed marketing industry, gaming, security, forensics, video conferencing, smart meetings, visual surveillance and anti-terrorism. It is a process in which the unique facial characteristics of a person are matched with the templates stored in a facial database [1]. Finding an automated solution for the face recognition problem is not a trivial task due to various factors including variable lighting effects [2], different facial expressions and postures [3] in different images of the same person. Face recognition techniques can be classified into three groups; feature-based, holistic, and hybrid of these two [1]. In face recognition process, facial features’ extraction process plays an important role and it involves a number of decisions like the selection of appropriate features, description, and Special Issue on Computational Intelligence Paradigms in Recommender Systems and Online Social Networks representation of these features. The fundamental goal is to represent patterns with the most significant features from the feature set yet with minimal loss of important facial information [4]. The FR methodology is highly dependent on the quality of such feature selection that serves as an input to the classifier. A feature extractor of good quality must be capable of extracting the discriminant features in unconstrained situations like outdoors, variable illumination, variation in poses, and facial expressions etc. Majority of the holistic feature extraction technique uses a combination or variations of Neural Networks, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) [5] etc. PCA-based face recognition has the advantages of simplicity and efficiency but is vulnerable in the presence of different lighting conditions, translation-variance, and different poses [6]. LDA-based techniques are suitable candidates for feature extraction and dimensionality reduction. These are generally considered better than the conventional PCA-based techniques and are also able to solve the illumination problem [7]. Transform domain-based techniques extract the major features of a facial image by performing the transformation from one coordinate system to a new orthogonal coordinate system resulting in the reduction of dimensionality and compact representation of data.