A new approach to face recognition based on features fusion

Abstract

In this paper, a theoretically efficient method is developed for face recognition. It is based on two dimensional principal component (2DPCA) analysis and extended local binary pattern (Extended LBP, ELBP) texture. First, the ELBP operator is employed to extract the local texture of the face images. Second, 2DPCA is used to reduce the dimensionality of the extracted feature and get the optimal projection space. Finally, the nearest distance classification is used to distinguish each testing image. The method has been ac-cessed on ATR-Jaffe and AR face databases. Results demonstrate that the proposed method is obviously superior to PCA and 2DPCA, and its recognition rate is more stable than PCA. Meanwhile, the proposed method has strong robustness against illumination and facial expression changes. INTRODUCTION In the field of biometrics recognition, face recognition has good application prospects in the field of identification, security monitoring and human-computer interaction. Factors affecting the face recognition mainly lie in: the changeable facial expressions and pose [11]; the same face will change with age; two dimensional face images are susceptible to be affected by illumination when they are filmed; recognizing a specific face involves a lot of knowledge, such as image preprocessing , pattern recognition and computer vision. Feature extraction is key to face recognition. Global feature extraction methods include principal component analysis (PCA) [9], independent component analysis (ICA) [6] and linear discriminate analysis (LDA) [12] under the conditions that samples obey the multivariate normal distribution. The advantages of PCA method is to get global features which can represent the characteristics of the face image by K-L transform, the disadvantage is that two-dimensional image matrix need to be transformed into one-dimensional vector, resulting in a huge amount of computation. Meanwhile PCA has a bad robustness under the illumination, pose and facial expressions. Jian Yang developed a novel two-dimensional PCA (2DPCA) [10] method. Compared with global features, local feature has a strong robustness against the changes of illumination and pose. The common local feature extraction methods include local binary pattern (LBP) [2, 8], scale invariant feature transform (SIFT) and so on. LBP is an effective texture extraction method and has been extensively exploited in many applications, for instance, image analysis, texture classification [1, 5, 7], environment modeling and other fields [3]. Classifiers commonly used in face recognition are: minimum distance method, nearest neighbor classifier, support vector machine classifier and BP neural network method. Based on 2DPCA, this paper proposed a fusion feature extraction method of 2DPCA and extended LBP (ELBP) texture, in the stage of classification a reliable algorithm—the nearest classifier—is utilized to identify a specific face image, these measures effectively reduce the influence of illumination, facial expressions and pose changes and improve the recognition rate. FUSION FEATURE EXTRACTION OF 2DPCA AND EXTENDED LBP The proposed method This paper aimed to improve face recognition rate under lighting changes and facial expressions. 2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 2015) © 2015. The authors Published by Atlantis Press 1398 Figure 1 illustrates the flow chart of the improved method. Figure 1. The structure diagram of face recognition of the proposed method First, preprocess images to enhance information. Second, use LBP to describe faces. Third, obtain the optimal projection space by 2DPCA. Fourthly, project test set and training set into the optimal projection space and obtain a feature matrix for each image. Finally, employ a classifier to classify, summarize the recognition rate and do some analysis comparing with other similar algorithms. LBP face appearance descriptor LBP-based feature extraction operator is computationally efficient statistical characteristics that can distinguish different objects in the same image in microcosm form [6], and is not sensitive to changes of gray-scale and different lighting conditions. LBP operator represents texture by comparing the gray value of the center pixel with the gray-scale of its neighboring pixels. It has been proved a powerful approach to describe local structure [5]. The LBP pattern of each pixel is defined as: