Facial Expression Recognition Using Hybrid Discriminant Analysis

Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition. Although LDA and MPCA share common goals and methodologies, in previous research they have been applied separately and independently. In this paper, we propose an extension of LDA to multiple factor frameworks. Our proposed method, Multifactor Discriminant Analysis, aims to obtain multilinear projections that maximize the between-class scatter while minimizing the withinclass scatter, which is the same core fundamental objective of LDA. Moreover, Multifactor Discriminant Analysis (MDA), like MPCA, uses multifactor analysis and calculates subject parameters that represent the characteristics of subjects and are invariant to other changes, such as viewpoints or lighting conditions. In this way, our proposed MDA combines the best virtues of both LDA and MPCA for face recognition.

Wavelet transform decomposition of electroencephalogram (EEG) signals has been widely used for the analysis and detection of epileptic seizure of patients. However, the classification of EEG signals is still challenging because of high nonstationarity and high dimensionality. The aim of this work is an automatic classification of the EEG recordings by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis (PCA), feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compared with those obtained in other studies for the same dataset. However, this method is not meant to replace the clinician but can assist him for his diagnosis and reinforce his decision.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

Serum samples were studied using Raman spectroscopy and analyzed through the multivariate statistical methods of principal component analysis (PCA) and linear discriminant analysis (LDA). The blood samples were obtained from 11 patients who were clinically diagnosed with breast cancer and 12 healthy volunteer controls. The PCA allowed us to define the wavelength differences between the spectral bands of the control and patient groups. However, since the differences in the involved molecules were in their tertiary or quaternary structure, it was not possible to determine what molecule caused the observed differences in the spectra. The ratio of the corresponding band intensities were analyzed by calculating the p values and it was found that only seven of these band ratios were significant and corresponded to proteins, phospholipids, and polysaccharides. These specific bands might be helpful during screening for breast cancer using Raman Spectroscopy of serum samples. It is also shown that serum samples from patients with breast cancer and from the control group can be discriminated when the LDA is applied to their Raman spectra.

In this paper, a new framework for the automatic recognition of human emotions from speech was proposed. Besides auditory indicative features, selected prosodic and voice quality parameters were optimally combined with Mel frequency coefficients to perform an automatic emotion classification. For this purpose, the Emotion Prosody Speech and Transcript database, a certified speech corpus, was used throughout this study. An extensive set of experiments have been carried out in order to assess the effectiveness of this original mixture of prosodic, perceptual and auditory features to perform the emotion recognition task. These features were selected by using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) on the basis of their ability of discrimination. The selected features were used by the front-end processing stage of a hybrid Gaussian Mixture Model and Support Vector Machines (GSVMs) to perform the emotion classification. The results showed the effectiveness of the proposed feature extraction framework to discriminate between different human emotions when the LDA-PCA-GSVM classifier was used.

In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate.

Character recognition in low quality and low-resolution images is still a challenging problem. In this paper a gray-scale image based character recognition algorithm is proposed, which is specially suit to gray scale images captured from real world or very low quality character recognition. In our research, we classify the deformations of the low quality and low-resolution character images into two categories: (1) High spatial frequency deformations derived from either the blur distortion by the point spread function (PSF) of scanners or cameras, random noises, or character deformations; (b) Low spatial frequency deformations mainly derived from the large- scale background variations. The traditional recognition methods based on binary images cannot give satisfactory results in these images because these deformations will result in great amount of strokes touch or stroke broken in the binarization process. In the proposed method, we directly extract transform features on the gray-scale character images, which will avoid the shortcomings produced by binarization process. Our method differs from the existing gray-scale methods in that it avoids the difficult and unstable step of finding character structures in the images. By applying adequate feature selection algorithms, such as linear discriminant analysis (LDA) or principal component analysis (PCA), we can select the low frequency components that preserve the fundamental shape of characters and discard the high frequency deformation components. We also develop a gray- level histogram based algorithm using native integral ratio (NIR) technique to find a threshold to remove the backgrounds of character images while maintaining the details of the character strokes as much as possible. Experiments have shown that this method is especially effective for recognition of images of low quality and low-resolution.

Multimodal biometrics-based systems aim to improve the recognition accuracy of human beings using more than one physical and/or behavioral characteristics of a person. In this paper, different fusion schemes at matching score level and feature level are employed to obtain a robust recognition system using several standard feature extractors. The proposed method involves the consideration of a face–iris multimodal biometric system using score level and feature level fusion. Principal Component Analysis (PCA), subspace Linear Discriminant Analysis (LDA), subpattern-based PCA, modular PCA and Local Binary Patterns (LBP) are global and local feature extraction methods applied on face and iris images. In fact, different feature sets obtained from five local and global feature extraction methods for unimodal iris biometric system are concatenated at feature level fusion called iris feature vector fusion (iris-FVF), while for unimodal face biometric system, LBP is used to achieve efficient texture descriptors. Feature selection is performed using Particle Swarm Optimization (PSO) at feature level fusion step to reduce the dimension of feature vectors for improving the recognition performance. Our proposed method is validated by forming three datasets using ORL, BANCA, FERET face databases and CASIA, UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed matching score level fusion scheme using Weighted Sum rule, tanh normalization, iris-FVF and facial features extracted by LBP achieves a significant improvement over unimodal and multimodal methods. Support Vector Machine (SVM) and t-norm normalization are also used to improve the recognition performance of the proposed method.

A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to detect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.

Fluorescence excitation-emission matrix (EEM) and synchronous scanning fluorescence (SF), coupled with parallel factor (PARAFAC) analysis, principal component analysis (PCA) and Linear discriminant analysis (LDA) methods were used to differentiate 49 lager beer samples and monitor the effects of temperature and sunlight exposition on their composition. EEMs were decomposed into independent fluorescent components. The beer samples were characterized by the presence of excitation/emission (exc/em) peaks at 290/350, 315/345, 340/410, 375/455, 360/420, 400/460, and 437/525nm, which were ascribed, according to the known beer fluorescent components, respectively to aromatic amino acids, vitamin B6 (pyridoxal), vitamin B6 (pyridoxic acids), vitamin B3, iso-α-acids, vitamin B1, and vitamin B2. The variation of the relative concentration of iso-α-acids in the different beer brands presented the same trend with that of their relative IBU, thus revealing the potency of our method in the assessment of beer bitterness. The impact of temperature and sunlight was assessed by separately monitoring the modifications of the EEMs after 5h exposition to 40°C temperature and sunlight respectively. Noticeably a variation of the peaks intensity of the iso-α-acids, carbonyl and polyphenols compounds were observed, accompanied by a decrease of the alcohol content, thus indicating beer aging. This method can be useful for the identification and monitoring of beer state during the technological production cycle and storage. PRACTICAL APPLICATION: The present work demonstrates the potency of the fluorescence technique used together with chemometric methods to give valuable information on beer bitterness. Development of rapid quantitative methods for beer bitterness assessment is of great importance for brewing industries.

Remote sensing Hyperspectral Image (HSI) comprises significant information about the earth’s surface which is actually acquired by hundred of narrow and adjacent spectral bands. The intended performance of classification accuracy does not attain due to the volume of the original HSI dataset and the enormous quantity of spectral bands. As such, dimensionality reduction approaches using feature extraction and selection are typically adopted to enhance classification performance. The unsupervised Principal Component Analysis (PCA), as well as the supervised Linear Discriminant Analysis (LDA), are commonly used as linear feature extraction methods for feature subspace detection. However, due to considering the effects of global variation, both PCA and LDA fail to extract local characteristics of HSI. In this paper, we propose a segmented LDA-based (SLDA) feature extraction where we apply the LDA in a segmented way to extract better local characteristics as well as global characteristics from the HSI. Per-pixel classification using a Support Vector Machine (SVM) is applied to our proposed SLDA method, PCA, Segmented-PCA (SPCA), and LDA on the Indian Pines agricultural HSI dataset. The experimental results show that the overall classification performance of SLDA (90.60%) remarkably outperforms all the other investigated methods: PCA (85.55%), SPCA (86.96%), LDA (86.45%), and the complete original dataset without employing any feature reduction method (83.10%). The proposed SLDA also requires the least amount of space complexity in different implementation scenarios.

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods.

Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics.

In this study, we investigated the combined effects of MoS2 QDs' catalytic properties and the colorimetric responses of organic reagents to create a sniffing device based on the sensor array concept of the mammalian olfactory system. The aim was to differentiate the volatile organic compounds (VOCs) present in cigarette smoke. The designed optical nose device was utilized for the classification of various cigarette VOCs. Unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA) methods were employed for data analysis. The LDA analysis showed promising results, with 100% accuracy in both training and cross-validation. To validate the sensor's performance, we assessed its ability to discriminate between five cigarette brands, achieving 100% accuracy in the training set and 82% in the cross-validation set. Additionally, we focused on studying four popular Iranian cigarette brands (Bahman Kootah, Omega, Montana Gold, and Williams), including fraudulent samples. Impressively, the developed sensor array achieved a perfect 100% accuracy in distinguishing these brands and detecting fraud. We further analyzed a total of 126 cigarette samples, including both original and fraudulent ones, using LDA with a matrix size of (126 × 27). The resulting LDA model demonstrated an accuracy of 98%. Our proposed analytical procedure is characterized by its efficiency, affordability, user-friendliness, and reliability. The selectivity exhibited by the developed sensor array positions it as a valuable tool for differentiating between original and counterfeit cigarettes, thus aiding in border control efforts worldwide.

Local Discriminant Embedding (LDE) was recently proposed to overcome some limitations of the global Linear Discriminant Analysis (LDA) method. In the case of a small training data set, however, LDE cannot directly be applied to high-dimensional data. This case is the so-called small-sample-size (SSS) problem. In this paper, we introduce an Exponential Local Discriminant Embedding (ELDE) technique to overcome the SSS problem. The advantages of ELDE are that, compared with Principal Component Analysis (PCA) + LDE, the ELDE method can extract the most discriminant information that was contained in the null space of the locality preserving between-class and within-class scatter matrices. In addition, the proposed ELDE is equivalent to transforming original data into a new space by distance diffusion mapping, and then, LDE is applied in such a new space. As a result of diffusion mapping, the margin between samples belonging to different classes is enlarged, which is helpful in improving classification accuracy. The experiments are conducted on four public face databases, YALE, PIE, Extended Yale and PF01. Experiments conducted on real data show that the performance of ELDE is better than that of LDE and many state-of-the-art discriminant analysis techniques.