In Brief

An empirical study of the impact of masks on face recognition

Dimensionality reduction methods play an important role in face recognition. Principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference (CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when 2DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.

Although two-dimensional principal component analysis (2DPCA) extracts image features directly from 2D image matrices rather than one dimensional vectors, 2DPCA is only based on the whole images to preserve total variances by maximizing the trace of feature covariance matrix. Thus, 2DPCA cannot extract localized components, which are usually important for face recognition. Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition called two-dimensional nonnegative principal component analysis (2DNPCA) to extract localized components and maintain the maximal variance property of 2DPCA. 2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, 2DNPCA has both advantages of 2DPCA and NMF. Furthermore, 2DNPCA solves the time-consuming problem by removing the restriction of minimizing the cost function and extracting only the base matrix. The nearest neighbor (NN) classifier and linear regression (LR) classifier are used for classification and extensive experimental results show that 2DNPCA plus NN and 2DNPCA plus LR are both very efficient approaches for face recognition.

Big data refers to huge information whose quantity is beyond the capacity of the system to manage, process and capture. On the basis of the biological and behavioral characteristics of the human, information gathered from which person can be recognized and referred as biometrics. Example includes finger print, face, voice and behavioral analysis etc. Among these, face recognition will not make use of any physical contact with the biometrics system; it is more secure and effective. Hence face recognition becomes one of the best technologies in the biometrics field. Although plenty of efforts have been carried out in the field of face recognition, this work has mark with many challenges in general settings. The criteria behind the successful face recognition systems are developed only under constrained situations with small sized data bases. Hence there is necessary to work under general setting that fit to big data for face recognition. This paper, aims to increase performance and accuracy of face recognition by considering face spoofing attack (veracity). Result analysis of our proposed work ensures increase in accuracy compared to other methods.

Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition. With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved. FaceNet is a deep learning framework commonly used in face recognition in recent years. FaceNet uses the deep learning model GoogLeNet, which has a high accuracy in face recognition. However, its network structure is too large, which causes the FaceNet to run at a low speed. Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet. This article mainly does the following works: Based on the analysis of the low running speed of FaceNet and the principle of MobileNet, a lightweight FaceNet model based on MobileNet is proposed. The model would reduce the overall calculation of the network by using deep separable convolutions. In this paper, the model is trained on the CASIA-WebFace and VGGFace2 datasets, and tested on the LFW dataset. Experimental results show that the model reduces the network parameters to a large extent while ensuring the accuracy and hence an increase in system computing speed. The model can also perform face recognition on a specific person in the video.

This paper proposes a two-dimensional color uncorrelated principal component analysis algorithm(2DCUPCA) for unsupervised subspace learning directly from color face images. The 2DCUPCA can be used to explore uncorrelated properties among color-based features, which contain minimum redundancy and ensure linear independence among features. Furthermore, the proposed 2DCUPCA provided the theoretical foundations analysis and proved the uncorrelated property between color-based features in theory. This makes it sure that the extracted features directly from three color image matrices will be uncorrelated. Finally, experimental results on the AR and FRGC-2 color face databases show that 2DCUPCA achieves better recognition performance than other color face recognition methods.KeywordsPattern recognitionface recognitionuncorrelated principal component analysisfeature extraction

Face recognition is a biometric technology used to identify individuals based on facial feature information. Previous studies of face recognition used features including the eye, mouth and nose; however, there have been few studies on the effects of using other facial components, such as the eyebrows and chin, on recognition performance. We measured the recognition accuracy affected by these facial components, and compared the differences between computer-based and human-based facial recognition methods. This research is novel in the following four ways compared to previous works. First, we measured the effect of components such as the eyebrows and chin. And the accuracy of computer-based face recognition was compared to human-based face recognition according to facial components. Second, for computer-based recognition, facial components were automatically detected using the Adaboost algorithm and active appearance model (AAM), and user authentication was achieved with the face recognition algorithm based on principal component analysis (PCA). Third, we experimentally proved that the number of facial features (when including eyebrows, eye, nose, mouth, and chin) had a greater impact on the accuracy of human-based face recognition, but consistent inclusion of some feature such as chin area had more influence on the accuracy of computer-based face recognition because a computer uses the pixel values of facial images in classifying faces. Fourth, we experimentally proved that the eyebrow feature enhanced the accuracy of computer-based face recognition. However, the problem of occlusion by hair should be solved in order to use the eyebrow feature for face recognition.

Image fusion technology has been widely applied in the field of face recognition in recent years. Most scholars focus on the improvement and innovation of fusion methods to optimize the performance of face recognition. However, face localization before image fusion also determines the quality of fusion images and recognition performance. To further improve the quality of the fused images, this paper performed Canny edge alignment on the thermal images and visible images in the Tufts Face Database, located and cut out the face part according to the projection spectrum. Then, several traditional fusion techniques, such as Weighted Average technique and Select Minimum technique, were used to fuse the two images. Finally, the IR3C algorithm was used to calculate the recognition accuracy of each fusion method. The final results showed that using Canny edge detection method and pixel projection method could achieve high-quality face localization and segmentation, and further improve the quality of the fusion image and the accuracy of face recognition, especially the Weighted Average technique and Select Minimum technique showed 100% recognition accuracy.

In this paper, based on the study of the Two-Dimensional Principal Component Analysis (2DPCA), Two-Dimensional Principal Component Analysis (2DPCA) and fuzzy set theory, we propose a integrated face recognition algorithm based on wavelet subspace. This method can make good use of the advantages of each single method, and also can make up for the defect of each other. The comparison of the results of the different methods identification effect on the ORL、YALE and FERET face database show, the integrated method proposed in this paper improves the recognition rate, and it also reduces the training and classification time as well. Key word: face recognition; Two-Dimensional Principal Component Analysis (2DPCA); Two-Dimensional Linear Discriminant Analysis (2DLDA); fuzzy set theory; feature extraction be divided into several categories:the method based on geometric features、the method based on model、the method based on the statistical、the method based on neural network method and the method of combining multiple classifiers, this paper mainly focuses on the method based on statistics. The method based on statistical faced the image as random vector, thus using some statistical methods to analyze the face model, the most representative methods include the eigenface method based on principal component analysis which proposed by Turk M (2)

The equivalence of two-dimensional PCA to line-based PCA

In this paper, the improved face recognition method based on two-directional 2DPCA (two-dimensional principal component analysis) in each block of face images is proposed. Firstly, the face image is divided into several sub-images, and then the sub-image features of each corresponding block are extracted by two-directional 2DPCA according to the number of sub-images. Finally, using the support vector machine to improve the recognition rate. Experimental results on ORL face database and YALE face database show that the proposed method is superior to any other 2DPCA methods in face recognition rate.

The paper made trend analysis on global patent in the field of face recognition by taking advantage of retrieval and analysis tool of Innography, conducted specific comparison on high-intensive patents of three countries, China, America and Korea which were top three in number of global patent applications, and analyzed existent advantages and gap in patent application and layout in China. Although our country started later in the field of face recognition, it had been prosperous development trend on number of patent applications due to technology innovation and policy support. In terms of high-intensive patent, there was a certain gap with the United States. Accordingly, This paper proposed suggestions for face recognition technology in China: To improve quality of patent application; to strengthen cooperation among enterprises, universities and other scientific research institutions in order to realize a win-win situation; To fully cultivate the talents of face recognition and artificial intelligence.

With the improvement of security requirements of individuals, businesses and countries, the identity authentication system based on face recognition technology is applied widely. The identity methods based on single theory, due to a variety of factors, have their own inherent limitations. In order to improve the efficiency and accuracy of face recognition, based on fusion of wavelet packet sub-images, a face recognition algorithm named FW-FLD is proposed in this paper by combining PCA (Principal Component Analysis) and FLD(Fisher Linear Discriminant). Firstly, the training samples are decomposed using wavelet packet. The fusion weights of the decomposed sub-images are calculated according to their energy distribution and the weighted fusion images are obtained, which reserve the images characteristic in the frequency. Then the features of the fused images are extracted using PCA, and the optimized Fisher space is constructed using FLD. Finally, face images are classified by measuring the projection coefficients of optimized training samples and testing samples in Fisher space. Experimental results on CMU PIE, JAFFE and AR face databases show that the proposed algorithm is robust, and can adapt to face recognition with various illumination, facial expressions and gestures. Compared with other algorithms, it not only improves the face recognition rate, but also has a higher time efficiency.

A feature extraction method of palmprint recognition based on Two-Dimensional Principal Component Analysis (2DPCA) is proposed in this work. A series of experiments were performed on the PolyU- Online- Palmprint ---Database with a nearest neighbor classifier and cosine distance. The recognition rate is 99.14%. The 2DPCA method has more recognition accuracy and more computationally efficient than PCA, especially in the small training samples. At the same time the selection of threshold has been researched in different application systems.

This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT) to the local face regions. In this paper, the new three hybrid color spaces, YSCr, Z n SCr, and B n SCr, are firstly constructed using the Cb and Cr component images of the Y Cb Cr color space, the S color component of the HSV color spaces, and the Z n and B n color components of the normalized XYZ color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT) face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.