Face Recognition Using Eigenfaces, Geometrical PCA Approximation and Neural Networks

In flotation production, the surface texture of the flotation froth is an important visual information reflecting the ore grade, which is closely related to the flotation conditions and directly reflects the mineralization degree (grade level) of the froth layer. Aiming at the problem that the texture feature extraction of the existing flotation froth image does not consider the froth surface particle, a new local texture feature extraction method based on PCA (Principle Component Analysis) algorithm is proposed and a new texture feature granularity is defined. Firstly, the froth image is segmented, the region of interest is extracted and the particle factor is defined. Then the texture feature is extracted by PCA algorithm and the correlation between particle size and zinc concentrate taste is analyzed. The experimental results verify the algorithm effectiveness and indicating that it can more accurately regulate the position of minerals and guide flotation production.

The principle component analysis(PCA) is a common feature extraction method in machine learning and pattern recognition approaches. PCA has been used in many applications, and face recognition in which specific faces are recognizing in an images database is one of the popular applications. The default distance metric which has been used with PCA based-face recognition is Euclidean distance. In this study, we have tested the Mahalanobis distance instead of Euclidean, and PCA based on Mahalanobis distance suggested a better performance on our students images database with highest recognition rate. However, we proposed weighted and normalized Mahalanobis distance based PCA-face recognition(PCA_WNMD). The proposed algorithm (PCA_WNMD) showed an improvement in faces recognition rate when tested on our students images database compared to PCA based on Mahalanobis and default Euclidean distances.

In the conventional eigenface method, the principle component analysis (PCA) algorithm associates the eigen vectors with the changes in illumination. In this paper, we propose an improvement of facial image association for face recognition using a cognitive processing model. This method is based on the notion of multiple-phase associative memory. The Essex face database is used to verify our model for facial image recognition and compare the results of face recognition with conventional eigenface method. The simulation results show that the proposed cognitive processing model approach results in better performance than that of the conventional eigenface approach; while the computational complexity remains of the same magnitude as that of the eigenface method.

The web pages are considered as the main source of the available and provided information that is characterized by variation in its content. The facial recognition plays a key role in knowledge management and identity authentication systems. Although the rapid advance of the web technologies and face recognition systems, the improvement of real-time performance is still the bottleneck. The main objective of this study is to propose a real-time face retrieval system as a service over cloud computing based on a web face crawler. The proposed architecture ensures that the total response time is reduced and the resource utilization is optimized. The web crawlers fetch web pages and extract images in elastic storage over the cloud. Then the collected images are used to extract human faces and to prepare the faces images by succeeding phases to be ready for recognition and identifying the matched face of the collection. The proposed service depends on Principle Component Analysis (PCA) algorithm for feature extraction and dimensionality reduction. Furthermore, K-Nearest Neighbors (KNN) is used to classify the crawled facial images over cloud resources. The experimental results investigated that an enhancement of crawling speed is achieved by increasing the crawler instances. Moreover, the accuracy is enhanced in the face recognition based on the Euclidean over other metrics such as Manhattan and Cosine dissimilarity.

Face recognition is a very old and popular biometric problem. In the last two decades, several challenges occur in face recognition such as expression, occlusion, illumination, pose variation, and low-quality image. Also, several techniques exist, such as Principle Component Analysis (PCA), Linear Discriminate Analysis (LDA), Local Non-Negative Matrix Factorization (LNME), Independent Component Analysis (ICA), and other variants, which finds good results under constrained scenarios. But, they give poor accuracy in an unconstrained scenarios such as illumination, pose variation, and occlusion. As Face recognition biometrics has gained great importance in occlusion condition, for example, a terrorists cover his face using facial accessories like sunglasses, scarves, or masks. This occlusion face recognition is categorized into two parts. 1) Feature extraction without occlusion detection face recognition. 2) Feature extraction with occlusion detection face recognition 3) occlusion recovery face recognition. Feature-based and fractal-based methods consider features around the eyes, nose, or mouth region to be used in the recognition face of the algorithm. This paper describes the different techniques and databases to handle the partial occlusion problem.

Infrared (IR) Face recognition has become an area of growing interest since it can operate in dim light and total darkness. This paper introduces a comparative study between the infrared face recognition systems using the principle component analysis (PCA) and the Zernike moments (ZM) techniques. The performance is evaluated according to the recognition rate, time consumption, and immunity to both Salt & Pepper and Gaussian noise. The analysis shows that the PCA technique has the same performance as the ZM technique if a large size dataset is used. On the other hand, the ZM technique outperforms the PCA technique when using a small size dataset, but it consumes a time approximately equal to four times that required by the PCA technique. The simulations also show that the ZM technique outperforms the PCA technique in the presence of both Salt & Pepper and Gaussian Noise types at various values of noise variance.

Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature extraction algorithms namely Principle Component Analysis (PCA), Sparse PCA, Kernel PCA and Incremental PCA. These algorithms are integrated with machine learning models to improve HF prediction. The performance of all these integrated models are evaluated by analyzing Cleveland heart failure database. Experimental results pointed out that Kernel PCA algorithm integrated with linear discriminant analysis model and Sparse PCA integrated with Gaussian Naive Bayes (GNB) model offers 91.11% of HF classification accuracy. Hence, based on the experimental results it is shown that Kernel PCA and Sparse PCA are suitable feature extraction methods for HF data.

Individuals with cerebral visual impairment (CVI) frequently report challenges with face recognition, and subsequent difficulties with social interactions. However, there is limited empirical evidence supporting poor face recognition in individuals with CVI and the potential impact on social–emotional quality-of-life factors. Moreover, it is unclear whether any difficulties with face recognition represent a broader ventral stream dysfunction. In this web-based study, data from a face recognition task, a glass pattern detection task, and the Strengths and Difficulties Questionnaire (SDQ) were analyzed from 16 participants with CVI and 25 controls. In addition, participants completed a subset of questions from the CVI Inventory to provide a self-report of potential areas of visual perception that participants found challenging. The results demonstrate a significant impairment in the performance of a face recognition task in participants with CVI compared to controls, which was not observed for the glass pattern task. Specifically, we observed a significant increase in threshold, reduction in the proportion correct, and an increase in response time for the faces, but not for the glass pattern task. Participants with CVI also reported a significant increase in sub-scores of the SDQ for emotional problems and internalizing scores after adjusting for the potential confounding effects of age. Finally, individuals with CVI also reported a greater number of difficulties on items from the CVI Inventory, specifically the five questions and those related to face and object recognition. Together, these results indicate that individuals with CVI may demonstrate significant difficulties with face recognition, which may be linked to quality-of-life factors. This evidence suggests that targeted evaluations of face recognition are warranted in all individuals with CVI, regardless of their age.

This paper presents a combination techniques of appearance-based and feature-based feature extraction on the T-Zone face area to improve the recognition performance. This study shows that the T-Zone area and the combined technique provides a significant impact on the face recognition rate. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. High recognition rates are obtained when compared to other face recognition methods of the same class. Our result shows of 7% improvement compared with PCA and 2% improvement compare with Sub-Holistic PCA. Our studies proves that the T-Zone area which is consisting of eyes and nose region is a ‘significant facial region’, and we also show that LBP can easily be combined with PCA to reduce the length of the feature vector, while the recognition performance is improved.

In modern digital era authentication has been done using biometric recognition. This biometric includes finger print, footprint, facial recognition, DNA of a person’s gene, hand palm print and eye’s iris recognition. The widely used among these is finger print and iris recognition. In this work we proposed a biometric recognition using footprints of a person. Earlier work deals with capturing footprint on a paper or on a surface. This won’t give us accurate foot print, since it depends on nature of the surface, quality of the paper and proper placement of the foot to give good foot print impression. To avoid all these we proposed a touch less method to obtain foot prints. The footprint can be obtained using any digital camera. We can take footprint image in many angles to conform the individuality of a person. In this work we used Principle Component Analysis (PCA) for pattern recognition and feature extraction. Then the SVM classifier split the patterns in to relevant classes. In early stage of our work itself we got remarkable quality and it is comparatively better than conventional footprint images obtained using paper or surface

The objective of the paper is face recognition using PCA and Bit plane slicing. It made a study on the dimensionality reduction on bit plane of images for face recognition. The proposed frame work would aid in robust design of face recognition system and addressed the challenging issues like pose and expression variation on ORL face database. It is in contrast to PCA on the image the design of PCA on bit plane reduces computation complexity and also reduces time. In the proposed frame work image is decomposed with the help of bit plane slicing, the feature have been extracted from the principle component analysis (PCA).

Face represents complex, multi dimensional, meaningful visual stimuli. Computational models for face recognition represent the problem as a high dimensional pattern recognition problem. This paper introduces an innovative facial identification method using eigenface approach on volume-based graphics rather than 2D photo-images. We propose to convert polygon mesh surface to a volumetric representation by regular sampling in a volumetric space. Our motivation is to extend existing 2D facial analysis techniques to a 3D image space by taking advantage of use of the volumetric representation. We apply principle component analysis (PCA) for dimensionality reduction. Face feature patterns are projected onto a lower dimensional PCA sub-space that spans the known facial patterns. 3D eigenface feature space is constructed for face identification.

Delayed recognition of emotional facial expressions in Bell's palsy

The spike sorting, including neuronal spike waveform acquisition and classification, is one of the important procedures in neuronal information processing, and its feature extraction and recognition are the basis of the above issues. Based on Locality Preserving Projection ( LPP) algorithm, an unsupervised feature extraction and classification algorithm was proposed. The neighbor parameter was selected automatically, the distribution dispersion standard was obtained according to the original data set, and the features of extraction results in spikes were separated effectively. The application in simulation and real experimental data show that, the proposed method based on the LPP can more effectively extract and separate features of spikes in comparison of the traditional Principle Component Analysis ( PCA) algorithm.

Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are known as classical techniques used in face recognition. Both 2-D PCA, 2-D LDA are based on 2-D matrices as opposed to classical PCA and LDA which are based on 1-D vectors. In current work Discriminative Component Analysis(DCA) simultaneous projection of probe image into PCA and LDA face spaces for face recognition is proposed. Also Alternate DCA is proposed. The study is conducted on AT&T database (formerly ORL database) and study reports are encouraging as that of other analysis.