Local Fisher Discriminant Embedding for face recognition

In the present work, appearance-based face recognition method called the Laplacianface approach is used. The face images are mapped into a face subspace for analysis by using Locality Preserving Projections(LPP). The technique is different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space. The main goal of LPP is to preserve neighbourhood structure of the data set optimally and hence obtain a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the Eigen functions of the Laplace Beltrami operator on the face manifold. Hence, by using this approach undesired variations because of facial expression, changes in lighting conditions, and pose may be eliminated or reduced. Performance analysis of face recognition is carried out on standard databases using both LPP technique and PCA technique. For comparison purpose, PCA with ANN classifier based on Back Propagation Feed Forward Neural Network is also developed and being used for training the input face images and then testing. LPP approach outperforms PCA with ANN, and provides better face representation and also achieves lower error rate.

Face recognition technology has evolved as an enchanting solution to address the contemporary needs in order to perform identification and verification of identity claims. By advancing the feature extraction methods and dimensionality reduction techniques in the application of pattern recognition, a number of face recognition systems has been developed with distinct degrees of success. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP preserve the local structure of face image space which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). This paper focuses on a systematic analysis of locality-preserving projections and the application of LPP in combination with an existing technique This combined approach of LPP through MPCA can preserve the global and the local structure of the face image which is proved very effective. Proposed approach is tested using the AT & T face database. Experimental results show the significant improvements in the face recognition performance in comparison with some previous methods.

Quaternion-based improved LPP method for color face recognition

Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC) $^{2}\text{A}$ and Block FLDA.

Locality-preserving projection (LPP) is a promising manifold-based dimensionality reduction and linear feature extraction method for face recognition. However, there exist two main issues in traditional LPP algorithm. LPP does not utilize the class label information at the training stage and its performance will be affected for classification tasks. In addition, LPP often suffers from small sample size (3S) problem, which occurs when the dimension of input pattern space is greater than the number of training samples. Under this situation, LPP fails to work. To overcome these two limitations, this paper presents a novel supervised regularization LPP (SRLPP) approach based on a supervised graph and a new regularization strategy. It theoretically proves that regularization matrix [Formula: see text] approaches to the original one as the regularized parameter tends to zero. The proposed SRLPP method is subsequently applied to face recognition. The experiments are conducted on two publicly available face databases, namely ORL database and FERET database. Compared with some existing LDA-based and LPP-based linear feature extraction approaches, experimental results show that our SRLPP approach gives superior performance.

Learning representations that disentangle hidden explanatory factors in data has proven beneficial for effective pattern classification. Slow feature analysis (SFA) is a nonlinear dimensionality reduction technique that provides a useful representation for classification if the training data is sequential and transitions between classes are rare. The pattern discrimination ability of SFA has been attributed to the equivalence of linear SFA and linear discriminant analysis (LDA) under certain conditions. LDA, however, is often outperformed by locality preserving projections (LPP) when the data lies on or near a low-dimensional manifold. Here, we take a unified manifold learning perspective on LPP, LDA and SFA. We suggest that the discrimination ability of SFA is better explained by its relation to LPP than to LDA, and give an example of a situation where linear SFA outperforms LDA. We then propose a novel supervised manifold learning architecture that combines hierarchical nonlinear expansions, as commonly used for SFA, with supervised LPP. It learns a nonlinear parametric data representation that explicitly takes both the class labels and the manifold structure of the data into account. As an experimental validation, we show that this approach outperforms previously proposed models on the NORB object recognition dataset.

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.

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.

Currently, facial recognition systems need to reveal a person’s face to perform recognition. However, some people currently wear masks to prevent respiratory infections. Therefore, this will affect the efficiency of some face recognition systems. Therefore, in this study, we propose a face recognition and body temperature measurement system, and propose an integrated system for masked face recognition, mask detection, and body temperature detection. The proposed system can be used to recognize unconcluded facial images, and we use methods capable of simulating occluded images. The resulting images are used to train the neural network. Experimental results show that the accuracy of naked face recognition reaches 99.79%, and the accuracy of masked face recognition reaches 99.4%. In addition, the mask detection accuracy rate reaches 99.6%. Therefore, the system can improve the accuracy of face recognition and provide efficient security inspection results.

Dimensionality reduction technologies are very important for pattern representation and recognition. Among them, locality preserving projection (LPP) is a manifold dimensionality reduction scheme and has been successfully applied to face recognition. However, LPP is an unsupervised linear approach, its performance will degrade for classification tasks. Especially, when the dimension of input space is greater than the number of training data, singularity problem will occur and LPP cannot be implemented directly. To tackle the draw backs of LPP algorithm, this paper proposes a novel regularized LPP(RLPP) approach using supervised graph and regularization technique. The proposed RLPP method has been tested and evaluated with two public available databases, namely ORL and FERET databases. Experimental results show that the proposed RLPP approach surpasses Laplacianface and Direct-LPP (DLPP) methods.

LPP solution schemes for use with face recognition

Face recognition has received significant attention because of its numerous applications in access control, security, and surveillance system. In real-world scenarios, uncontrollable lighting conditions, a variety of posture and facial expressions, and noisy facial images can degrade the face recognition accuracy. Hence, the research based on the HOG algorithm and pre-processing implementation framework processing framework to improve face recognition accuracy is proposed. This proposal consists of four stages where the first stage is to build a dataset of 15 subjects and has five series of multi-poses of facial images. The second stage is focused on enhancing the pre-processing framework that consists of denoising colored, illumination normalization, and facial alignment algorithms. For the third stage, the HOG algorithm is utilized as a feature descriptor to detect the face. The fourth stage is implementing the deep convolution neural network to evaluate the accuracy of face recognition. From the observation, the improvement in the accuracy rate is up by 4.37% after the enhancement of the pre-processing framework.

In order to search for an efficient feature extraction approach for face recognition,a method based on feature level fusion was presented.This method integrated Locality Preserving Projection(LPP) with Maximum Margin Criterion(MMC).Firstly,LPP was performed on training samples set,so the projection of each training sample on LPP subspace could be got.Further,MMC algorithm was performed on all the obtained projections to get more efficient discriminant features for recognition.Nearest Neighborhood(NN) algorithm was used to construct classifiers.The experiments on the ORL face database show that the recognition rate of the proposed method is high when pose,illumination condition,face expression and training sample number change.

Academic Information System (Sistem Informasi Akademik aka SIAKAD) Online of Universitas Prima Indonesia (UNPRI) is one of the applications used to facilitate the administration process of lectures which includes the filling process of study plan cards (Kartu Rencana Studi aka KRS), study result cards (Kartu Hasil Studi aka KHS), class schedules, submission of research titles, seminars, and other processes. SIAKAD UNPRI can be accessed by students, lecturers, and academics where every user has a password that has been encrypted to maintain the security of information from people who are not responsible, password security using the encryption method needs to be changed regularly, but there are still many students, lecturers and academic community who are reluctant to change passwords. To improve the security verification stage for SIAKAD users, we propose a face recognition feature approach. Face recognition is a feature that allows the identification of someone from a digital image or video. The way the facial recognition method works is by comparing face data from the camera or images with images that were previously stored in a database. In this study, the Kohonen SOM method is proposed for face identification based on the feature extraction approach of discrete cosine transform (DCT), linear discriminant analysis (LDA) and principal component analysis (PCA) to improve the security of UNPRI SIAKAD users. The analytical framework is done by requiring students to do face taking, where each student will save 5 (five) faces extracted with facial features using the DCT, LDA and PCA model approach, feature extraction results are used as input to the Kohonen SOM network for training and testing facial recognition, then analysis of the effect of DCT, LDA and PCA feature extraction on the Kohonen network on facial recognition accuracy.

In this paper, a new Face image classification or Recognition method based on Two Dimensional Discrete Cosine Transform (2D - DCT) with Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) is proposed. This method consists of three steps, i) Transformation of images from special to frequency domain using Two dimensional discrete cosine transform ii) Feature extraction using Linear Discriminant Analysis and iii) classification using Probabilistic Neural Network. Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Two Dimensional Discrete Cosine transform and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network (PNN) is a promising tool and gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from AT&T (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers. The main advantage of this method is its high speed processing capability and low computational requirements in terms of both speed and memory utilizations.